Understanding the Factors Affecting Faculty-led Admissions Committee Construction at Highly Regarded Canadian Public Universities
Bibliographic record
Abstract
Student demographic underrepresentation limits the ability of many members ofunderrepresented minority (URM) communities to realize the social mobility benefits of an elite university educational experience. Underrepresentation of these communities is particularly acute at Canada’s most highly regarded universities, which I define as those highly-ranked by publications popular among higher education stakeholders. One possible way to address underrepresentation is through the work of admissions committees. This exploratory, qualitative study used social reproduction theory to investigate the constructionof faculty-led admissions committees at two highly regarded Canadian universities to better understand the factors that are important to faculty leaders as they consider faculty colleagues for admissions committee service. The study also sought to understand the influence a faculty leader’s social identities and lived experiences play in their approach to committee construction. Study participants were fourteen faculty members with administrative leadership experience. Thetwo-part participant interview included (1) a simulation of an undergraduate interdisciplinary honors admissions committee construction, in which participants selected candidates from among imaginary colleague bios, and (2) a semi-structured interview during which they reflected on their simulation choices and their prior experience and knowledge of admissions committee construction at their home institutions. Iterative Thematic Inquiry (Morgan & Nica, 2020) was used to identify themes in the interviews,and dual scaling (Nishisato & Nishisato, 1994) provided insight into the selections made by participants during the admissions committee construction simulation, with the aim of connecting their backgrounds to their selections of simulation colleagues. The thematic inquiry results suggest that a colleague’s lived experience and identity, their willingness and capacity to serve, and their records of student engagement are factors that faculty leaders consider when evaluating colleagues for admissions committee service. Study participants relied on their networks and considered the interpersonal skills of potential admissions committee candidates to be important, but preferred to construct admissions committees without undue influence from top university leadership. The study’s findings also suggest that a faculty leader’s race, gender identity, academic background, and their leadership role may influence their evaluations of potential committee candidates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".