Four ways forward in studying sex-based harassment
Bibliographic record
Abstract
Purpose The purpose of this paper is to identify promising themes of the papers in the special issues of Equality, Diversity and Inclusion dedicated to advancing scholarship on sex-based harassment. Design/methodology/approach A conceptual overview of the research pertaining to these themes and an analysis of the special issues papers' contributions to these themes. Findings Four themes that represent important but relatively neglected lines of inquiry into sex-based harassment are identified. These are (1) the psychology of harassment, (2) organizational culture and networks, (3) the invisible majority and (4) the importance of collective action. Originality/value The paper offers an expert perspective on the state of research related to sex-based harassment and four themes that are important to moving it forward.
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 | 0.111 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.009 | 0.049 |
| Scholarly communication | 0.034 | 0.038 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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".