Bibliographic record
Abstract
For the last two years I have taught an e ight -month senior u n d e r g r a d u a t e course on political correctness in the psychoiog-y d e p a r t m e n t at my college titled PSY 3831:,: Psvcholocy and Ideology--the Study of Political Correctness. The fact that this course became possible, in the cu r r en t a tmosphere of passionate dew)tion to the cause of ideological conformi ty and censorship, is due to a nttnlher of part icular events and personalities without which the course would never have been approved. I would like to begin my expose, in a few broad strokes, by out l in ing the pregnancy stage of the course that tinally resuhed in its birth. Thereaf ter , I will describe its infancy and speculate about later deve lopmenta l stages. My hope is that this course, which is now being taught in a psychology depa r tmen t , will soon be of fered by many o the r depa r tmen t s in the country. It is my t in ' ther hope that in the not-too-distant t'uttare it will be taught only as a history course and will then be cen te red on a topic that will have been utterly defea ted and relegated to the follies of the embarrass ing past.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".