Bibliographic record
Abstract
There are kinds of dialogue that support social justice and others that do the reverse. The kinds of dialogue that support social justice require that anger be bracketed and that hiding in safe spaces be eschewed. All illegitimate ad hominem/ad feminem attacks are ruled out from the get-go. No dialogical contribution can be down-graded on account of the communicator’s gender, race, or religion. As well, this communicative approach unapologetically privileges reason in full view of theories and strategies that might seek to undermine reasoning as just another illegitimate form of power.On the more positive side, it is argued in this paper that social justice dialogue will be enhanced by a kind of “communicative upgrading,” which amplifies “person perception,” foregrounds the impersonal forces within our common social spaces rather than the “baddies” within, and orients the dialogical trajectory toward the future rather than the past. Finally, it is argued in this paper that educators have a pressing responsibility to guide their students through social justice dialogue so that their speech contributes to the amelioration of injustice, rather than rendering the terrain more treacherous.
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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.042 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.018 |
| 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".