An interview with Gerald Cupchik: Equity, diversity and inclusion
Bibliographic record
Abstract
How should we address equity, diversity, and inclusion issues in the ‘Post-COVID era’? Some students just want the degree, whereas others miss the social intimacy of classroom experiences. In this interview, we address a dissociation between university administrations with top-down, ideologically driven agendas, and the lived experiences of students. Students become immersed in diversity by participating in classes based on shared interests that cut across backgrounds and reflect experiential learning; moving from ‘cliques to networks.’ Inclusion cannot be mandated by the university and formally required of lecturers. Rather, it reflects a student’s feeling of belonging based on acceptance by others in the classroom setting and this is something that lecturers can foster. Equity is a more delicate theme tied to past exclusions that touch many communities. Gatekeepers have historically excluded students based on race or cultural affiliation. Attempts to redress this imbalance for specific communities can forget the historical exclusion of others. My approach favors ‘inclusive authenticity,’ whereby students are in touch with their heritage, and ‘reflective awareness,’ a sensitivity to the political dynamics that surround them. We can move from ‘surface to depth,’ both as institutions and individuals by fostering critical thinking and listening to the voices of students.
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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.016 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.012 | 0.028 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".