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Record W4283033908 · doi:10.1177/10982140211056913

Developing Evaluation Approaches for an Anti-Human Trafficking Housing Program

2022· article· en· W4283033908 on OpenAlex
Rebecca J. Macy, Amanda Eckhardt, Christopher J. Wretman, Ran Hu, Jeong-Suk Kim, Xinyi Wang, Cindy Bombeeck

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAmerican Journal of Evaluation · 2022
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFormative assessmentProtocol (science)Program evaluationEvaluation methodsHuman traffickingPublic relationsBest practicePsychologyProcess managementPolitical scienceEngineering ethicsBusinessMedicineEngineeringPublic administrationPedagogyAlternative medicine

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.965
Threshold uncertainty score0.528

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.247
GPT teacher head0.471
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it