Bibliographic record
Abstract
An argument can be made that hateful rhetoric and group versus group conflict has increased in Canada in part due to the lack of identification in the Criminal Code of identity-based soft violence, thereby inadvertently providing perpetrators with the incentive to continue with their activities unpunished (Meyers, 2019). Kelshall has defined these unrecognized acts of hate as soft violence, which includes “actions that stop short of criminally identified violence...and highlight the superiority of one group over another without kinetic impact" (as cited in Kelshall & Meyers, 2019, p. 40). The damage created by soft violence is incalculable, as its harmful effects range from instilling fear within the individual victim or targeted identity-based group, to the polarization of society that damages cohesion within the general public (Kelshall & Meyers, 2019). Furthermore, it might be useful to consider the damage of soft violence on researchers of such content. Therefore, the Predicting Escalation research project first focuses on the impact of analyzing hateful content on the researchers themselves. The following progress report outlines the supportive literature, research challenges, and research findings that have been collated thus far.
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 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".