Policy to Stem Violence, Discrimination, Harassment and the Abuse of Power
Bibliographic record
Abstract
PreambleThis policy has been developed in light of the College's Mission Statement, the Human Resource Management Policy, the Quebec Charter of Human Rights and Freedoms (CQLR, ch.C-12) and the Act Respecting Labour Standards (CQLR, ch.N-1.1).Dawson College recognizes that all its employees and students are entitled to a respectful and harmonious work and/or study environment free from violence, discrimination, all forms of harassment, and the abuse of power, where respect of the individual's dignity, physical and psychological integrity are safeguarded.To this end, Dawson College will take all reasonable measures to prevent incidents of violence, discrimination, harassment and the abuse of power, and, when informed of such incidents whether informally or formally in writing, will intervene to address them.Article 1 Objectives 1.01 While it is understood that it is impossible to guarantee the absence of violence, discrimination, harassment or the abuse of power in any environment, this policy is intended first, to promote understanding and prevention, and, second, to provide a means of addressing these types of incidents should they occur. 1.02To this end, the College will establish a committee to promote education and the prevention of incidents of violence, discrimination, harassment and the abuse of power.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.092 | 0.014 |
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".