Bibliographic record
Abstract
Recent student demands within the academy for "safe space" have aroused concern about the constraints they might impose on free speech and academic freedom. There are as many kinds of safety as there are threats to the things that human beings might care about. That is why we need to be very clear about the specific threats of which the intended beneficiaries of safe space are supposed to be relieved. Much of the controversy can be dissolved by distinguishing between "dignity safety," to which everyone has a right, and "intellectual safety" of a kind that is repugnant to the education worth having. Psychological literature on stereotype threat and the interventions that alleviate its adverse effects shed light on how students’ equal dignity can be made safe in institutions without compromising liberty. But "intellectual safety" in education can only be conferred at the cost of indulging close-mindedness and allied vices. Tension between securing dignity safety and creating a fittingly unsafe intellectual environment can be eased when teaching and institutional ethos promote the virtue of civility. Race is used throughout the article as the example of a social category that can spur legitimate demands for "dignity safe space."
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".