Online targeting of researchers/academics: Ethical obligations and best practices
Bibliographic record
Abstract
ABSTRACT Targeted online harassment of academics has been on the rise in the US and internationally. Such harassment ranges from online threats and hacking to doxxing and organized campaigns to discredit one's work. It has now become an often‐unrecognized part of the job for academics—particularly socially marginalized academics and those who study controversial topics such as race, ethnicity, gender, and sexuality—to work to protect themselves from such attacks. With this increase in intimidation attempts, new questions for the profession arise regarding what skills we should be instilling in trainees, whether professional association and academic units should be taking explicit positions or action on researcher/faculty harassment, and the obligations of our employers with regard to defending faculty, staff, and students. This panel will describe targeted online harassment of academics, discuss models for individual and institutional response, and raise questions for the profession as a whole. Sponsored by SIG ED.
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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 | 0.131 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".