Bibliographic record
Abstract
Abstract High-profile controversies have created an impression that expressive freedom is imperilled on university campuses in North America. Analyses of this alleged campus crisis typically focus either on the negative psychosocial characteristics of those who oppose potentially harmful expression or on the cynical ways that expressive freedom can be invoked to normalize harmful expression. Conversely, I argue that theories of harm are key to understanding the contemporary discourse and politics of expressive freedom on campus. To shift the frame of analysis, I critically analyze three interrelated theoretical concepts that feature elastic conceptualizations of harm and are consequential for expressive limits in an academic environment: epistemic injustice, argumentational injustice and epistemic exploitation. I argue that all three concepts require a distinction between testimony and argumentation in order to better balance protection from harm, on the one hand, and expressive freedom and open inquiry, on the other.
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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.116 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".