MétaCan
Menu
Back to cohort
Record W3191605514 · doi:10.1093/sw/swab039

Safety Issues for Social Workers Engaging in Anti-Trafficking Work

2021· article· en· W3191605514 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSocial Work · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicStalking, Cyberstalking, and Harassment
Canadian institutionsnot available
Fundersnot available
KeywordsSocial workOfficerCriminologySocial WelfareState (computer science)DutyWelfarePublic relationsPolitical scienceSociologyLaw

Abstract

fetched live from OpenAlex

It is critical for state governments and social service agencies to promote, monitor, and protect the safety of social workers in all workplaces—especially those in anti-trafficking practice. According to NASW (2013, p. 5), a considerable number of social workers have been targeted in verbal and physical assaults. Some have even been injured, and others have lost their lives “in the line of duty.” Social workers have been murdered in the United Kingdom, Canada, the United States, and Malaysia (ABS-CBN News, 2009; Dickerson, 1998; Donoghue, Dover, & Burbank, 2015; Epoch Newsroom, 2018; Smith, 2019; Turner, 2019). Specifically, a social welfare officer named Finardo Cabilao was found dead in his home in 2009. He fought against the exploitation and trafficking of Filipino women in Malaysia and was murdered because of his anti-trafficking work (ABS-CBN News, 2009). As a social worker, Cabilao’s death...

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0040.000
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 head0.041
GPT teacher head0.352
Teacher spread0.311 · 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