Bibliographic record
Abstract
At its core, academic knowledge production is predicated on Western notions of knowledge historically grounded in a Euro-American, White, male worldview. As a component of academic knowledge production, scholarly publishing shares the same basis of Whiteness. It excludes knowledge that doesn’t conform to White, Western notions of knowledge, forces conformity to (and therefore reinforcement of) a Western standard of writing/knowledge, and leads to a reverence of peer-reviewed literature as the only sound source of knowledge. As a tool of scholarly publishing and the editorial process, blind peer review, though perhaps well-intentioned, is fraught with problems, especially for BIPOC researchers and writers, because it fails in its intended purpose to drastically reduce or eliminate bias and racism in the peer review and editorial processes; shields peer reviewers and editors against accusations of bias, racism, or conflicts of interest; and robs BIPOC, and particularly Indigenous, writers and researchers from having the opportunity to develop relationships with those that are reviewing and publishing their work.
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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.019 | 0.060 |
| Scholarly communication | 0.023 | 0.013 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".