Gaining a Fuller Picture of Sex Trafficking in Manitoba: A Case Study of Narrative-Based Research Utilizing 'Low Tech' Thematic Analysis
Bibliographic record
Abstract
This article explores narrative-based, person-centered research, carried out by the author for his PhD dissertation, titled Modern Day Slavery and the Sex Industry: Raising the Voices of Survivors and Collaborators While Confronting Sex Trafficking and Exploitation in Manitoba, Canada. The article describes interview dynamics considered and accounted for, including positionality of the researcher and the narrative-based research methodology. The author provides detailed description of the grounded, inductive, old school, low technology data analysis process used, some of the challenges encountered, and recommendations for similar studies in future. The key challenges arose from the positionality of the researcher and the need to protect the participants from potential repercussions. The recommendations relate to the relevance of narrative-based research, limited transferability of the results of such research, and the value of a more open-ended interviewing style.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.033 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".