Obstacles to diversification: Lived experiences of visible minority applicants and faculty members in psychology.
Bibliographic record
Abstract
The profession of psychology demonstrably lacks diversity, with inequitably low numbers of visible minorities represented in the field. Although equity, diversity, and inclusion as a social advocacy movement has received increasing attention within the profession and at the university level, changes are thus far too small to be noticeable. Little is known about the lived experiences of minority applicants applying to professional psychology graduate programs nor the experiences of faculty members involved in student selection processes. We interviewed eight unsuccessful minority applicants, and eight faculty members affiliated with Canadian professional training programs to identify obstacles in diversifying the profession. Thematic analysis of interview data revealed that obstacles were multifaceted, ranging from unsuccessful minority applicants reporting lack of support during the application process and feeling worried about the impact of their identities to faculty members expressing insufficient resources to provide mentorship experiences to minority undergraduate students. We discuss the practical implications of the obstacles identified in relation to how to dismantle those systemic barriers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.016 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".