Bibliographic record
Abstract
In this paper, I explore a particular formation of institutional racism within academic organizations. First, I detail the recent positive recognition of systemic barriers to inclusion in Canada through the rhetoric and policies from national research funding agencies, university managements, and faculty unions. I go on to suggest, however, that there is a contradiction in the promotional framing of these commitments as ‘inclusive excellence’ because the discourse of excellence implies that the institution is already performing at peak function and hence needs no systemic organizational change. I argue that this contradiction undermines the development of genuine motivations to address exclusions and reduces equity policies to tokenistic promotional branding. The excellence discourse appeals to the vanity of the academics who are being encouraged to be more inclusive, a vanity of ‘excellence’ that is a manifestation of the broader epistemological understanding of our profession as both very intelligent and neutral or objective in our approach to generating and assessing knowledge. This professional epistemology anchors our understanding of why the profession looks the way it does: white ethnic dominance is taken as a reflection of objective merit, which then prevents any consideration of whiteness as a contributing privilege to entering and progressing through the academy. I term this equation of whiteness with our professional capacities as ‘professional snowblindness’ because it prevents recognition of the whiteness of the profession precisely through recourse to our professional skills and capacities. I argue that this ‘snowblindness’ is the particular formation of institutional racism in the academy and, crucially, that it needs to be named and discussed if we are to create genuine motivations for equity.
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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.031 | 0.052 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".