“Nice White Meetings”
Bibliographic record
Abstract
Although the issues of diversity and representation are often discussed within academic librarianship in Canada and the United States, the field has made little headway in being inclusive of the Black, Indigenous, and People of Color (BIPOC) who work within it. If academic libraries are to become truly authentic and inclusive spaces where BIPOC are central not only to shaping the values of a library but also to determining how those values are accomplished, we must examine the traditional ways in which libraries function. One of these traditions is a reliance on bureaucracy and its associated practices such as structured group work and meetings, which are presumed to be inherently neutral and rational ways of working. Critical examinations of bureaucracy within higher education reveal how its overadoption is absurdly at odds with the social justice–oriented missions of most libraries. Furthermore, not all who are involved in libraries are equally harmed through this overreliance on bureaucracy; this article employs Critical Race Theory to uncover the insidious and specific deleterious impacts bureaucracies can have on BIPOC library workers. The antithesis of a neutral system, bureaucracy instead functions to force assimilation into a system entrenched in whiteness.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.122 | 0.062 |
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".