Developing Evaluation Approaches for an Anti-Human Trafficking Housing Program
Bibliographic record
Abstract
The increasing number of anti-trafficking organizations and funding for anti-trafficking services have greatly out-paced evaluative efforts resulting in critical knowledge gaps, which have been underscored by recent recommendations for the development of greater evaluation capacity in the anti-trafficking field. In response to these calls, this paper reports on the development and feasibility testing of an evaluation protocol to generate practice-based evidence for an anti-trafficking transitional housing program. Guided by formative evaluation and evaluability frameworks, our practitioner-researcher team had two aims: (1) develop an evaluation protocol, and (2) test the protocol with a feasibility trial. To the best of our knowledge, this is one of only a few reports concerning anti-trafficking housing program evaluations, particularly one with many foreign-national survivors as evaluation participants. In addition to presenting evaluation findings, the team herein documented decisions and strategies related to conceptualizing, designing, and conducting the evaluation to offer approaches for future evaluations.
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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 | 0.430 | 0.328 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".