Ethnicity and Effectively Maintained Inequality in BC Universities.

Sweet, Robert ; Robson, Karen ; Adamuti-Trache, Maria 等

LABOR MARKETS IN CANADA INCREASINGLY reward knowledge and skill obtained through advanced training and education (Finnie et al. 2016). This has encouraged a steady rise in postsecondary education (PSE) aspirations across all sociodemographic dimensions--gender, socioeconomic status (SES) or class, and ethnicity (Davies 2005; Frenette and Zeman 2007; Krahn and Taylor 2005). Much of the demand for improved access to PSE is concentrated at the university level and provincial governments have responded by expanding their baccalaureate programs (Fisher et al. 2014).

Expansion of university programs in Canada parallels patterns seen in other countries where "high participation systems" have been established. For the most part, additional capacity has improved accessibility for many previously underrepresented groups (Marginson 2016). However, as such systems expand they tend to become differentiated in terms of institutional teaching and research resources, academic selectivity, and their relationship with the labor market (Davies and Zarifa 2012). Many prospective students are aware of such institutional differences and their aspirations are shaped accordingly (Baker 2014). These same features are more formally recognized in national rankings of universities published annually in such publications as US News and World Report, the Times Higher Education, and Maclean's. The resulting "prestige hierarchies" often shape students' (and their familys') aspirations to the point where gaining admission to the more prestigious and academically selective universities becomes the focus of intense competition (Findlay and Kohler 2010). As a consequence, youth from affluent families typically predominate in high-value institutions while women and many minorities are granted access to universities with less potential for social mobility (Marginson 2016; Marks 2013; Treventi 2013). In many instances, however, associations between institutional destination and gender or race ethnicity are moderated by academic factors (Gerber and Cheung 2008).

Enrollment in selective Canadian universities may reflect these general patterns but research is lacking. To our knowledge, only one study has assessed the relationship between social background factors and access to selective universities. Davies, Maldonado, and Zarifa (2014) examined university transitions of high school graduates in Toronto and report that, net of achievement and other relevant control variables, SES measures maintained a consistently positive relationship with selective enrollments while gender differences were not statistically significant. (1) Results for ethnicity were mixed. Some groups were less likely to gain admittance while others--notably Asian students--were more likely to enroll in top-ranked universities. The importance of ethnicity in gaining admission to selective universities reported by Davies et al. (2014) reflects the changed demographic composition of Metro Toronto resulting from large-scale settlement of culturally diverse immigrant groups. As similarly diverse youth populations also characterize other Canadian cities and regions (McAndrew et al. 2009), it would be important to determine whether ethnicity is equally salient in their selective admissions practices.

In this paper we use the British Columbia (BC) PSE system as a case study to examine relationships between institutional expansion, structural differentiation, and inequality of access along SES, gender, and ethnolinguistic lines. We conduct our analysis within an "effectively maintained inequality" or EMI theoretical framework (Lucas 2001) that links basic social background factors and students' admission to academically selective universities in the Province. Given the degree of cultural diversity among the student population in BC, especially in the Metro Vancouver region, ethnolinguistic characteristics are of particular interest in this analysis.

BACKGROUND

Structural Distinctions

Although less stratified than most other national PSE systems, Canadian higher education is becoming increasingly hierarchical (Davies and Zarifa 2012). These structural developments can be seen in the BC PSE system. Initially established as a "university-only" system, PSE in BC evolved through the 1960s to become a binary system consisting of two credential tiers--a traditional university sector and a vocationally oriented community college sector. The latter provided additional programming equivalent to the first two years of a baccalaureate degree. Students who enrolled in the colleges' academic track were able to transfer credits to a four-year university in order to complete their degree requirements. Beginning in the 1990s, the province expanded the sector by increasing capacity in existing universities and by assigning degree granting status to several community colleges that were then labeled "university-colleges" (Dennison 2006). In 2008, changes to the Provincial University Act formally conferred full university status on these institutions but with a unique mandate to focus on teaching excellence rather than research excellence. (2) These reforms created a clear distinction between the established research-intensive universities (RIUs) and the newer teaching- intensive universities (TIUs) derived from the university-college system. Importantly, two-year, academic transfer programs were retained in the community colleges and shared the curriculum and teaching focus of the expanded TIU institutions (Fisher et al. 2014). The RIU-TIU designations differentiated institutions in terms of their course offerings and supporting material resources comprising library holdings, research funding, and endowments. RIUs and TIUs were also contrasted in the publics' perception of a "prestige hierarchy" among institutions, reinforced by university rankings published in Maclean's and by the institutions themselves as each strived to enhance its reputation.

Inequality Theory

PSE expansion in Canada has improved educational opportunities for many underrepresented and minority groups (Carney Strange and Hardy Cox 2016). However, analyses of PSE pathways indicate those from socio- economically advantaged backgrounds are still more likely to attend university than community college (Frenette 2008). Such differences in the distribution of privilege across PSE credential tiers have been attributed to processes of "maximally maintained inequality" in which socially advantaged youth gravitate toward more advanced (and rewarding) credential tiers (Raftery and Hout 1993). More recent research indicates that youth from high-income families and those whose parents are highly educated are not only more inclined to opt for a university degree than a community college diploma but also are more likely to attend universities that are well-resourced and selective in their admissions practices (Lucas 2001). In this theoretical formulation, students with advantaged backgrounds who become concentrated in the most favored institutions or programs of study within a credential tier are seen to have "EMI". EMI theory thus describes an additional horizontal sorting process in which socially advantaged youth distribute themselves across institutions based on perceived qualitative differences that are associated with value--either in the postsecondary experience itself or realized upon graduation. From this perspective, university degrees are considered "positional goods"-- possessions that can leverage social or economic rewards but which require exclusivity to be effectively utilized (Shavit and Park 2016). Subsequent research on EMI has substantiated the relationship between privilege and access to selective institutions and to preferred fields of study (Jerrim, Chmielewski, and Parker 2015). However, there is an acknowledged need to incorporate the changing gender and ethnic demographic of university student populations in EMI research and, as well, tie these to equitable admissions policies (Bielby et al. 2014; Davies et al. 2014; Marginson 2016; Marks 2013; Reimer and Pollak 2010).

Admissions

Access to the PSE system is, in principle, based on "merit," which is typically determined by students' high school grade point average (GPA). In principle, academic achievement will offset any advantage based on SES, gender, or race ethnicity. While admission policies based on merit are essential to the promotion of equality of opportunity, previous predictive research demonstrates that sociodemographic factors are not entirely accounted for by achievement indicators like GPA or formal test scores (Finnie, Childs, and Wismer 2011).

Ethnicity has recently gained prominence in the public discourse around admissions to more selective university programs and institutions--as illustrated in the controversial Maclean's magazine article on "overrepresentation" of Asian students in Canadian universities (Findlay and Kohler 2010). The academic response to the Maclean's article consists largely of qualitative assessments and opinion pieces that characterize the PSE system as systemically biased (Cui and Kelly 2013; Gilmour et al. 2012). However, available empirical analyses demonstrate that Asian students are more likely than most other ethnic groups to pursue a university degree (Abada, Hou, and Ram 2009; Finnie and Mueller 2010; Krahn and Taylor 2005). Perhaps the more important finding is that academic achievement and PSE pathways of Asian students are quite varied across the regional and linguistic groups that comprise this ethnic category (McAndrew et al. 2009; Sweet et al. 2010; Thiessen 2009). Asian students' higher overall university participation rates may extend to selective institutions as suggested by Davies et al. (2014) but if so they are likely to be equally variable.

An additional academic consideration in PSE admissions is English language proficiency. Roessingh and Douglas (2012) report that minority language students often transition to PSE without acquiring sufficient competence in English to succeed in their course work. Instruction in the specific forms of English language used in classrooms is available to minority language students in K-12 schools but its effectiveness is dependent on several factors, including age of arrival or grade entry level that can limit opportunities to gain competence (Cummins 1983; DiCerbo et al. 2014). Although one might assume such language skills are incorporated in GPA measures, Childs, Finnie, and Mueller (2017) found that PISA language scores had an effect independent of GPA in predicting students' PSE participation. By extension, English language proficiency may be important in predicting admission to selective university programs.

PURPOSE

In this paper we examine transitions to RIUs and TIUs in BC for evidence of EMI processes, looking specifically at the degree to which ethnicity contributes to stratification. A particular issue to be addressed in this context is the variability in PSE access typically found across ethnic groups. This aspect of educational (in)equality is important in the Metro Vancouver region where recent immigration from Asian countries has created an ethnically diverse student population (Statistics Canada 2015). We specifically address issues of merit in the admissions process by examining the degree to which students' high school GPA mediates the relationship between university admission and social background, especially ethnicity. Additionally, we explore the role of English language proficiency in constraining or enhancing the university trajectories of high school graduates. Based on the available literature, we would expect to find in predicting RIU attendance from social background factors that: ethnicity is a significant predictor of RIU attendance; that considerable variation exists between ethnic groups in their RIU attendance; and that both GPA and English language proficiency only partially mediate this relationship.

METHOD

Data and Sample

We employed information from several BC Ministry of Education files to construct a longitudinal file that allows examination of individual students' progress through the K-12 and PSE systems. These include:

1. Student-level administrative data between 1993 and 2009.

2. Data on provincial Grade 10 and Grade 12 examinable courses (2004-2008).

3. Selected Statistics Canada census socioeconomic indicators at the school level (based on 2001 and 2006 Census).

4. Data from the Student Transitions Project (STP) files that include information on students' PSE entry date and institutional choices.

We selected a sample of high school graduates (n = 10,256) from a 1990 birth cohort who entered the BC school system before 2008/2009 and who graduated high school before 2012/2013. Because ethnic diversity is greatest among students located in the 11 Metro Vancouver school districts, the sample was restricted to this area. Further restrictions included the elimination of students who had transferred out of province or who were enrolled in "off-shore" schools. The Francophone school district was also removed from the sample. This district, although headquartered in Vancouver, is merely an administrative unit that serves students located throughout the province.

Variables

Table 1 lists the variables drawn from the combined BC Ministry files and used in predicting students' enrollment in an RIU. Our primary predictors are SES, gender, and ethnicity--the latter indicated by home language. We also estimate the effects of students' GPA and English language proficiency on RIU enrollments with the additional aim of observing the extent to which these variables qualify relationships between social background and enrollment. We adjust all effects with selected control variables related to PSE planning and the acquisition of English language proficiency.

University Type

The dependent variable in the analysis indicates whether a student enrolled in one of the 11 TIUs in the Province or in one of the four RIUs. Enrollment in this case refers to the first recorded entry to a baccalaureate program in the BC postsecondary system. University destinations are compared for students who attended public school in the Metro Vancouver area. This controls for geographic proximity between students' homes and the universities in which they enrolled. In contrasting RIU and TIU enrollments, we include in the latter category students who were enrolled in two-year baccalaureate transfer programs offered by four community colleges as their curricula and emphasis on teaching rather than research matches the four-year TIU mandate.

Home Language

In Canadian studies of PSE, race/ethnicity variables have been indicated in various ways--home language, region of origin (country), and self-identified race (Satzewich and Liodakis 2013). In this study, BC data contain information on the students' first or primary language spoken in their home. Because the pattern of ethnic diversity in Metro Vancouver has dramatically altered in recent years as a consequence of increased immigration from Asian countries, the study will focus on Asian home language groups and use English speakers as a "reference" or comparison group in the analysis. The Asian language groups are: Chinese, South Asian, Korean, Vietnamese, and Tagalog.

Socioeconomic Status

We employ a school-level measure of median family income as our proxy for each individual's SES indicator. The BC Ministry assigns median family income to individual schools based on information obtained from the 2001 and 2006 Statistics Canada census data containing students' home postal codes. From these we construct a derived income variable that is averaged over the schools attended from elementary to senior high school graduation--typically --two to three schools per student. Averaging across schools in this way effectively "weights" the income variable to reflect the broader K-12 schooling experience (Caro, McDonald, and Willms 2009).

Control Variables

Within limits imposed by using the Ministry's administrative data, we employed several controls in our analysis. These include variables related to academic planning and preparation as well as English language proficiency. Academic planning strategies involve decisions such as high school course selection choices typically involving either a science or humanities emphasis that prepares them to access particular university programs (Adamuti-Trache and Sweet 2014; Crosnoe and Johnson 2011). Similarly, the decision to attend private (or independent) schools is often based on their presumed ability to develop academic skills and shape attitudes favorable to further education (Van Pelt, Allison, and Allison 2007; but see Adamuti-Trache, Bluman, and Tiedje 2013; Frenette and Chan 2015). The timing of the transition from high school to PSE--either immediate or delayed--is also an aspect of planning. The reasons for delaying university entrance are varied. Financial limitations or the desire for a gap year in which to travel and gain a wider life experience are often cited (Brady and Allingham 2010; King et al. 2005). For some, the need to improve their academic standing or, in the case of minority language students, the need to improve language competence can require further preparatory study (Gunderson 2007).

The second set of controls involves variables that have been found in the literature to affect English language proficiency. Minority language students who enroll in BC schools and who lack language proficiency are typically assigned to an English language learner (ELL) class (BC Ministry of Education 2013). ELL program success has been linked to the age at which students first receive ELL instruction, presumably reflecting their developmental sensitivity to language acquisition (Corak 2012). Alternatively, late arrival may simply curtail the instructional time available to ELL students (Dooley and Furtado 2013; Gunderson, D'Silva, and Odo 2012).

Academic Performance

Academic achievement is the basis for admissions to all postsecondary institutions in BC and GPA requirements for university admissions are generally higher at RIUs than at the TIUs. We include a standard measure of high school GPA (1.0-4.0) in our analysis that is an aggregate of teacher-assigned grades and provincial exam marks based on a set of required courses set out in the "2004 Graduation Program" guide (BC Ministry of Education 2013). Provincial exams are administered in Science 10, Mathematics 10, Social Studies 11, and Language Arts 10 and 12.

Given the language of instruction in BC schools is English, there is a need for non-English speaking students to become competent in the vocabulary and language structures employed in the classroom (Cummins 1983; DiCerbo et al. 2014). We use students' provincial English 10 exam marks as an index of classroom language proficiency.

ANALYSIS AND RESULTS

To model attendance at a selective university using the BC data, we construct a series of logistic regressions appropriate to our dichotomous dependent variable (attending an RIU or TIU). Since our data nests students within high schools, we employ random intercept models (Robson and Pevalin 2016). We estimate the results in Stata 12 using the xtmelogit procedure (StataCorp, College Station, TX).

Table 2 presents the results of the multilevel logistic regression analysis. The reference (omitted) category for the dependent variable is TIU. All independent variables are treated as fixed effects and results are presented as odds ratios. School IDs from which students graduated are treated as a random, level 2 variable. School variance is reported along with various fit statistics. The intraclass correlation coefficient (ICC) for the null model indicates that about 20 percent of the variance in attendance at an RIU is attributable to differences between schools. This justifies retaining the hierarchical design in the analysis. Significant likelihood ratio tests and the log-likelihood pattern also suggest improved model fit with the addition of the high school achievement and English language measures (StataCorp, College Station, TX).

Model 1 in Table 2 shows the relationship between RIU attendance and the sociodemographic variables of interest in the study together with the various planning and language instruction controls. The median family income variable was a significant predictor in the model. This was expected given results from previous EMI research on the relationship between SES and access to selective universities (Jerrim et al. 2015). However, gender and ethnicity variables were also significant in this model. While these results illustrate the continuing importance of SES in EMI processes, they reinforce the more recent view that gender and ethnic characteristics also play a significant role in the prediction of a selective university attendance. In the present analysis, females were less likely than males to attend an RIU (odds ratio of 0.79). This is consistent with other research on the relationship between gender and access to selective or elite universities (Bielby et al. 2014). Odds ratios for the various home language groups are shown in Model 1. As expected, considerable variability is present across different ethnicities. Chinese, Korean, and Vietnamese speakers were more likely to attend an RIU, relative to English speakers. South Asian and Tagalog speakers, on the other hand, were less likely to attend an RIU relative to English-speaking students. These results are similar to previous studies of ethnic educational attainment and achievement with one exception--while other studies have found superior academic attainment by South Asian students in Ontario (Davies et al. 2014; Sweet et al. 2010), this does not apply to their attendance at selective RIUs in BC.

Model 2 includes a measure of student achievement (GPA) to examine the merit thesis. Given that admissions are decided largely by GPA, a strong relationship between RIU attendance and high school achievement was expected. High GPAs were, in fact, found to be significantly related to RIU attendance (odds ratio of 10.69). The results of this estimation indicate, first, that however essential GPA might be to our understanding of an equitable admissions process, it does not entirely discount the effects of social background factors on RIU attendance. Second, the mediating effect of GPA on the type of institution students attended varies across ethnic groups. The odds ratios of Korean and Chinese students increased only slightly relative to the English-speaking reference group while Tagalog and South Asian speakers' odds ratios remained significantly lower than the English-speaking group. The odds ratio for Vietnamese students was reduced to nonsignificance.

A measure of English language proficiency obtained from students Grade 10 English exam scores was introduced in Model 3. The effects of this variable were expected to be mediated by the relationship between students' RIU attendance and their GPAs but previous research suggested language proficiency would also complement curricular achievement (Childs et al. 2017). The results indicate English language proficiency does play a significant role in predicting RIU attendance, independent of GPA scores (odds ratio of 1.02). English language proficiency also alters the RIU attendance odds ratios for specific ethnic groups. When English 10 exam scores were set to their average value, Chinese and Korean students' odds ratios increased, while the South Asian odds ratio was no longer statistically significant.

The consequences of including GPA and English language proficiency measures in the model were to reduce the RIU attendance odds ratios of some ethnic groups of students while increasing the odds ratios of others. This is most apparent among Korean, Chinese, and South Asian students. Most students from these groups will meet (and often exceed) the GPA minimums for selective institutions. However, at least some individuals in these same groups exhibit language proficiency limitations that affect the likelihood of attending an RIU. In order to further explore this finding, we examine RIU attendance patterns across achievement and language proficiency distributions. Figures 1 and 2 illustrate the predicted probabilities of RIU attendance for each ethnic group based on a regression that adjusts for variables in Model 3.

In Figure 1, the constraints imposed by GPA admission requirements are apparent. Few individuals with a GPA less than 3.0 are expected to gain entrance to an RIU. It is only at this level of achievement that English speakers have a probability higher than 0.5 of attending an RIU. Chinese and Korean students, on the other hand, are likely to attend an RIU with the same probability but with GPAs of 2.5. The likelihood of attending an RIU increases for all ethnic groups where students have GPAs of 3.5. At this level of achievement, the probability of attending an RIU for students in every ethnic group (except Tagalog speakers) is at or above 0.6. Chinese and Korean-speaking students are, then, more likely than English speakers to attend an RIU at all levels of the GPA distribution.

Figure 2 illustrates the relationship between the English 10 exam measure of ethnic students' English language proficiency and their RIU attendance. These graphs depart quite markedly from the GPA plots across ethnic groups. For English speakers the relationship to RIU attendance is incremental and the probability of RIU attendance does not exceed 0.6 until English 10 exam scores approach 90 percent. This pattern is generally similar for all other ethnic groups except the Chinese and Korean students. The probability of RIU attendance for the latter two groups exceeds 0.6 at virtually every level of the English 10 marks distribution.

The results shown in Figures 1 and 2 are consistent with Childs et al.'s (2017) research on the relationship between GPA, PISA language scores, and university program access. In their study, Chinese and "Other Asian" students were more likely to attend university than other ethnic groups, irrespective of overall academic achievement or English language competence. In the present study, attendance at an RIU was largely constrained by GPA but less so by English language proficiency. While high GPAs and an indication of English proficiency may be stated prerequisites for admission to RIUs, these appear to be general rather than absolute requirements.

DISCUSSION AND FUTURE RESEARCH

The BC government has pursued the goal of improved PSE access primarily by expanding baccalaureate programs. While expansion has enhanced accessibility, it has been accompanied by structural reforms that distinguish RIUs from TIUs. In this context, the present study explored relationships between social background variables and admissions to the more selective RIUs within an EMI or EMI theoretical framework. Consistent with recent EMI research, we found SES, gender, and ethnicity to be significant predictors of RIU attendance, even after controlling for academic performance and other factors relevant to university program admissions. Our primary concern was, however, with the role played by ethnicity in accessing RIUs and with the state of equitable admission practices to selective universities in BC.

In a culturally diverse community such as Vancouver one would expect a degree of ethnic representativeness in admissions across the PSE system, including the RIUs. However, in our analysis, some ethnic groups were overrepresented while others were underrepresented. These differences remained even after statistically controlling for students' GPA as well as other relevant contextual factors. Davies et al. (2014) obtained similar results and suggest "status culture" differences explain the remaining disparities in admissions. Lareau and Weininger (2003) locate these differences in families, where they are expressed as parenting strategies designed to guide investment of resources in children's education. Specific parenting behaviors include: encouraging PSE aspirations in children, committing financial savings for their future PSE, engaging in high school curriculum planning that anticipates PSE enrollment, and monitoring academic effort and achievement in essential subjects (Sweet and Anisef 2005). Variants of effective parenting strategies are found across ethnic groups, many of whom are immigrants (Costigan, Hua, and Su 2010; Sweet, Anisef, and Walters 2010; Taylor and Krahn 2013). These families often establish links to the broader ethnic community as a means of generating "ethnic capital" that further supports children and youth in their efforts to prepare for university (Abada et al. 2009).

For minority language youth, acquiring English language proficiency is an additional challenge in preparing for PSE. As an essential dimension of school instruction, English language proficiency underlies academic achievement and, consequently, university admissions. In addition to high school GPA, we explored the relationship between language proficiency and RIU attendance across the various ethnolinguistic groups. The results suggest that RIU attendance is generally constrained for students with English language limitations. However, some individuals from ethnic groups most likely (statistically) to attend an RIU enroll in these selective institutions despite English language limitations and, consequently, may be at academic risk (Roessingh and Douglas 2012).

Extensions to EMI Theory

We suggest EMI research in the Canadian PSE context might be extended in three ways. First, research on educational inequality within the EMI framework has considered almost exclusively the relationship between SES and selective admissions. The demographic profiles of PSE students are, however, becoming more diverse as increasing numbers of women and members of various ethnic groups pursue PSE. Marks (2013), for example, suggests "... the EMI hypothesis underplays the role of other factors on educational transitions" (p. 1636). To accommodate an increasingly diverse student demographic profile, future research on PSE stratification might extend the EMI framework by tracing the intersections between ethnicity, gender, and SES (McCall 2005; Robson et al. 2014). Second, evidence of EMI can be found not only in students' institutional enrollment patterns but also in their field of study choices (Reimer and Pollack 2010; Thomsen 2015). This research direction might be further explored in relation to ethnic differences in STEM (science, technology, engineering, and math) programs (Adamuti-Trache and Andres 2008; Adamuti-Trache and Sweet 2014). Finally, the present study was confined to students entering the first year of a baccalaureate level program. Graduate and professional programs represent another credential tier where EMI processes could usefully be explored in relation to ethnicity (Zarifa 2012).

Admissions Policy Development

Our analysis addresses some of the concerns with the merit concept as used in university admissions. It is apparent from this and other studies that, in addition to GPA, personal characteristics and situations play a role in students' access to RIU universities. Efforts to expand the criteria used in admissions beyond GPA may capture some of these individual differences. Individual RIUs have implemented what are termed "broad-based admissions" policies that include a mix of GPA and a written response (in English) outlining the individual's aspirations, nonacademic activities, and community service. As well, specific university departments may include interviews in their admissions procedures (Arida 2013). Proponents argue that alternate admissions criteria reward individual uniqueness and in this way contribute to diversity in the student population. They are not, however, free from the criticism that institutions may attempt to "shape the entering class" by weighting admission decisions in favor of a particular group (Sternberg 2010). At the present time broad-based admissions play a relatively minor role in admission decisions at most institutions (Furuta 2017). This is due not only to cautious implementation policies but also to technical challenges involving complex issues of measurement and interpretation (Duckworth and Yeager 2015). Further research that considers developmental characteristics and intellectual growth from a linguistic and cultural identity perspective has been proposed as necessary to inform and advance equitable admission practices (Arnett 2003; Guiffrida 2009; Norton and Toohey 2011).

Alternative admissions policies are also politically contentious. This was evident in the public debates and academic responses to the Maclean's "Too Asian" article. Here, critics argued such measures were assumed to reinforce existing systemic discrimination (Cui and Kelly 2013). Much of this criticism concerned the characterization of Asian students as a "model minority" that is academically industrious but socially disengaged (Costigan et al. 2010; Cui 2015; Yu 2012). The construction of such a limiting social identity potentially disadvantages applicants to universities that rely on broad-based admissions criteria. However, an emphasis on obvious (or even more subtle) markers of difference between students overlooks other explanations. Potvin (2008), for example, suggests the "cultural proximity" of different ethnic student groups can lead to ethnic tensions. In the competition for admission to ranked universities, Asian students may be perceived as a threat to other students not because they are so different but because they are so alike in their aspirations and behavior.

Critiques of PSE admissions policies need to acknowledge the full range of relevant research. This would include not only the qualitative studies mentioned above but also established quantitative work on PSE admissions. The latter offers a somewhat different view. For example, previous analyses of inequality based on Canadian national-level survey data have concluded that social and institutional barriers are not obvious constraints on the educational attainment of immigrant-ethnic youth, at least when compared to nonimmigrant, English-speaking students (Davies and Guppy 1998; Finnie and Mueller 2010). The same patterns are seen in more recent analyses of competitive admissions to selective universities. Davies et al. (2014), for example, found distinct advantages for immigrant- ethnic youth in gaining access to better resourced and more prestigious Ontario universities. Our analysis of BC data employed a more detailed ethnic sample yet obtained similar results albeit with considerable variability in RIU admissions within and between particular Asian language groups.

To conclude, we have examined the postsecondary transitions of students in BC for evidence of EMI processes in accessing selective universities in that Province. Consistent with research in other jurisdictions where university capacity has been expanded and where institutional resource and prestige distinctions have developed, we found that competition for access continues to favor those with social advantage while limiting opportunities for women. As generally reported in the EMI literature, these outcomes reflect established patterns of social reproduction. However, changes in the student demographic in BC have meant ethnicity is now an important factor in gaining access to selective universities. EMI in this context is likely tied more to the goal of social mobility than social reproduction (Davies et al. 2014). Our results indicate that many Asian-origin groups are successful in pursuing mobility through attendance at highly regarded universities. In examining the relationship between ethnicity and attendance at selective institutions we nevertheless found marked variability in enrollment among these groups. This ethnic "diversity within diversity" (Fleras 2015) combined with the continuing trend toward PSE expansion and university differentiation (Jonker and Hicks 2016) suggests the task of formulating admissions policies that balance social inclusion goals with academic selectivity will become increasingly complex.

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Robert Sweet

Lakehead University

Karen Robson

McMaster University

Maria Adamuti-Trache

University of Texas at Arlington

We wish to acknowledge the support of the BC Council on Admissions and Transfer and the Social Sciences and Humanities Research Council.

Robert Sweet, Faculty of Education, Lakehead University, 2885 Sunnyside Road, Anmore, BC, Canada V3H 4Y7. E-mail: rasweet@lakeheadu.ca

(1.) Where Davies et al. (2014) control for academic achievement their "female" regression coefficient is not statistically significant, a result consistent with established and current research findings (Bielby et al. 2014).

(2.) University seating capacity, which had been gradually increased in the 1990s, was suddenly and significantly expanded starting in 2005--establishing a trend that continues (Fisher et al. 2014, Fig. 2.14).

Caption: Figure 1 RIU Attendance Probabilities Based on GPA

Caption: Figure 2 RIU Attendance Probabilities Based on Language Proficiency Table 1 Descriptive Statistics University type Teaching intensive Research intensive Independent variables (means/proportions) (means/proportions) Social background Home language South Asian 13 5 Chinese 13 41 Korean 2 6 Tagalog 4 1 Vietnamese 2 2 English 66 44 Gender (female) 59 52 Family income (CDN$) 66,136 65,662 Control variables HS course choice (science) 72 92 School type (public) (a) 90 88 Transition Direct 49 84 +1 year 18 6 +2 years 12 5 +3 or more years 21 5 ELL class (ever enrolled) 31 50 Grade at school entry Elementary (Gr K-7) 93 86 Junior high (Gr 8-9) 3 6 Senior high (Gr 10-12) 4 8 School performance High school GPA (1-4) 2.86 3.33 English 10 exam mark 73.30 78.21 (0-100) Enrollment sample 4,049 6,207 Note: (a) Number of high schools (public and private) = 144. Table 2 Hierarchical Logistic Regression Predicting RIU Attendance Independent variables Odds ratios Null Model 1 Fixed effects Home language (English = ref) South Asian 0.552 *** Chinese 2.763 *** Korean 3.237 *** Tagalog 0.520 *** Vietnamese 1.109 *** Gender (male = ref) 0.790 *** Median family income 2.668 *** Course choice (humanities = ref) 2.853 *** School type (public = ref) 1.193 ns Transition (direct = ref) +1 year 0.200 *** +2 years 0.214 *** +3 or more years 0.145 *** ELL class (never = ref) 1.144 ns Entry grade (elem = ref) Junior high (Gr 8-9) 1.121 ns Senior high (Gr 10-12) 1.188 ns High school GPA English 10 exam mark Constant 0.3395 *** 0.000 *** Random effects Level 2 variance 0.841 0.615 Model fit LR chi sq 1,456.64 *** ICC 0.204 0.157 Log-likelihood -6,358.12 -5,208.83 Independent variables Odds ratios Model 2 Model 3 Fixed effects Home language (English = ref) South Asian 0.749 * 0.779 ns Chinese 2.811 *** 3.045 *** Korean 3.350 *** 3.751 *** Tagalog 0.480 *** 0.471 *** Vietnamese 1.182 ns 1.206 ns Gender (male = ref) 0.525 *** 0.519 *** Median family income 2.655 ** 2.599 ** Course choice (humanities = ref) 1.409 *** 1.454 *** School type (public = ref) 1.085 ns 1.067 ns Transition (direct = ref) +1 year 0.250 *** 0.247 *** +2 years 0.314 *** 0.320 *** +3 or more years 0.246 *** 0.248 *** ELL class (never = ref) 1.228 ns 1.293 * Entry grade (elem = ref) Junior high (Gr 8-9) 1.289 ns 1.527 ** Senior high (Gr 10-12) 1.507 ** 1.925 ** High school GPA 10.685 *** 8.355 *** English 10 exam mark 1.023 *** Constant 0.000 *** 0.000 *** Random effects Level 2 variance 0.706 0.685 Model fit LR chi sq 1,375.59 *** 1,433.65 *** ICC 0.172 0.172 Log-likelihood -4,521.04 -4,492.01 Notes: ICC, intraclass correlation coefficient; LR, likelihood ratio; ref, reference group; ns, nonsignificant. * p < .05, ** p < .01, *** p < .001.