Non-State Torture and Sexualized Human Trafficking:A Grassroots Science Framework
Bibliographic record
Abstract
This paper presents our individual/collective experiences and knowledge co-constructed through our involvement in non-State torture and anti-trafficking movements. The purpose of this paper is to help people critically understand torture and human trafficking of women in the Western countries using a case example from Canada presented in a webinar which is now reflected in this paper. Using grassroots science as a theoretical framework, we share our experiences and knowledge generated from our involvement in the anti-trafficking movement and the lived experiences in this critical reflective paper. Although we share some key findings from the research for in-depth discussion, we claim this paper is a reflective theoretical paper. In this article, we (Jeanne, Linda, Rita, and Jeanette) first begin by sharing our own social locations together with our collective journey to the anti-trafficking movement and the process of our involvement in the development of this paper which includes the social location of Jeanette Westbrook who attended the webinar. This paper is structured in five sections, and they include: (1) Historical and a brief review of non-State torture and sexualized human trafficking; (2) theoretical framework; (3) knowledge generation; (4)actions in practice; (5) discussion/conclusion
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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.015 | 0.118 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".