NOT FAR ENOUGH: HOW WORKPLACE HARASSMENT POLICIES FAIL TO PROTECT SCHOLARS FROM ONLINE ABUSE
Bibliographic record
Abstract
Over the last decade online spaces and digital tools have become a central part of scholarly work and research mobilization (Carrigan, 2016). However, the integration and reliance on these technologies into scholars’ work lives have heightened their online visibility, which has opened the door to new experiences of online abuse. Previous research shows that online abuse has negative impacts on scholars’ work, and that they are left to deal with the consequences of online abuse primarily on their own, with little support from their institution (Authors, 2018a; 2018b). Given the importance of online spaces/tools in scholars' lives and the detrimental impacts of harassment, colleges and universities must recognize the risks associated with online visibility and have policies in place that address those risks. In this paper we analyze 41 workplace policies that deal with harassment and discrimination from Canadian Universities and Colleges to understand what these institutions propose to do about online abuse. We use Bacchi’s (2012) ‘What’s the problem represented to be?’ (WPR) approach. This approach encourages examination of the assumptions and conceptual logics within the framing of a problem in order to understand implicit problem representations. Early analysis identified two problems common across the 41 policies that limit their ability to offer protection and/or support in many cases of online abuse: the first limitation focuses on who the policies apply to, and the second on where the policies apply.
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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 | 0.029 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.025 | 0.038 |
| Scholarly communication | 0.032 | 0.017 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".