Bibliographic record
Abstract
Sexual violence is an expansive term defined as any sexual act, comments or advances directed toward a person using coercion and aggressive, threatening, exploitive and manipulative behaviors (Kilpatrick, 2004). Reports of sexual violence have become a growing cultural focus in the recent years, with emergent attention to prevention in various contexts including the workplace (McDonald,2012), the military (Hillman, 2009), the elderly (Burgess & Morgenbesser,2005), and sport (Cense & Brackenridge, 2001; Holman,1995, Lenskyj,1992). Sport constitutes a unique environment through which sexual violence is allowed to inconspicuously fester. Sexual violence in sport is generally viewed as the abuse of power by an individual in a position of authority, such as a coach, over an athlete, with less research looking at sexual violence between athletes. In this research, semi-structured interviews were conducted with 8 athletes from a range of sports and sport levels. Interviews were transcribed verbatim and analyzed using a combination of inductive and deductive coding techniques. This presentation will highlight the existence of sexual violence between athletes, particularly in the form of hazing practices. Sexually exploitative hazing practices overlap with other forms of violence, such as physical and emotional violence (Stuart, 2013), yet they are not often considered within the definition of sexual violence. This presentation will examine how a setting of unequal power between athletes combined with the normalcy of hazing practices creates an unrestricted and potentially dangerous environment for athletes (Kirby & Wintrup, 2002).
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".