Exposing Exceptionalisms: B(e)aring Complicities and Framing Resistances
Bibliographic record
Abstract
Exceptionalisms are reductive, short-sighted, and often convoluted rationalizations for refusing relational accountabilities. They systematically deliver narrowly conceived benefits to some at great expense to others who are habitually held from public view and voice. In neoliberal times, excuses for ignoring damage and justifying harms are legion. Our planet is choking on the standard business practice of externalizing costs while permitting pollution, social ills, and health consequences to pile up in the lives of marginalized peoples, species, and places, with complicit nation states increasingly ill-equipped to address the fallout. Some exceptionalisms, like the “doctrine of discovery,” are perpetrated for centuries with virtual impunity, masquerading as sacred edict until the mass graves of children surfacing from residential school grounds reveal assimilative evils that are more difficult to ignore for those who have benefitted most.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.059 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".