Bibliographic record
Abstract
Given the recent number of hate-related incidents that have come to public attention, there is a significant need to collect and track these incidents in order to capture and share trends with the wider public. Outside of official hate crime data (such as annual government reports), incidents fueled by hate (but that are not crimes) often go undocumented. To address this gap, the Alberta Hate Crimes Committee – a Canadian coalition of law enforcement, government, and non-governmental organizations – developed the StopHateAB.ca website. The purpose of the StopHateAb.ca website is to fill this gap by creating a space to capture hate incidents to document and make accessible information related to hate incidents. This article will describe the development of the online hate incident reporting tool StopHateAB.ca. Through a discussion of the strengths and challenges of creating an online hate incident reporting platform, this paper will highlight the importance of innovative responses to counter hate and bias by making hate visible. As this article highlights, making hate visible forces communities to engage in joint conversations about hate and bias to support strategies that foster a public social environment of justice, equity, and human rights.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".