Bibliographic record
Abstract
In recent weeks, the world of gambling research has been shaken by a "#MeToo" moment.Founded by African-American activist, Tarana Burke, in 2006 as a way to connect survivors of sexual assault to other women and resources for healing, MeToo was popularised as a hashtag in 2017 with revelations of heinous abuses of power by Hollywood film producer Harvey Weinstein and casino magnate, Steve Wynn, as well as many other high profile men in the worlds of business, politics, entertainment and academia.(Hess, 2017, Mansfield, 2019) Gambling' studies' #MeToo moment involved a senior male academic researcher as the subject of a complaint against a colleague from another country and university.He was accused of sexual stalking and -following media publicity -resigned from his university.The complainant was an early career researcher with a capacity building role related to gambling research in her university.She had allegedly experienced intense and unrelenting sexual attention for nearly two years after terminating earlier consensual interactions with a man who she had initially valued as a mentor.After her new partner sent angry texts demanding that the senior researcher desist, the latter told the complainant that he would not only cease contact, he would also cut professional ties with her, including involvement in forthcoming research proposals.It was at this point that she decided to lodge a complaint.She explained to a media source that gambling research is a relatively small and close-knit community and that bad relations with her former mentor were likely to irreversibly damage her career.We do not want to dwell on the individuals involved in this case.Instead we cite these details as a starting point to encourage further research on characteristics of gambling research which reproduce structures of social injustice.We are unconvinced by explanations of this case that point to malicious intentions or individual pathology as the primary cause of physical, sexual or psychological harassment and violence.The naming and shaming and punitive treatment of individuals whose unethical behaviours are exposed can bring an immediate sense of relief or vengeance.But it can also paradoxically entrench injustice, by appearing to have expunged or resolved more intractable
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 | 0.045 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.069 | 0.095 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".