MétaCan
Menu
Back to cohort
Record W4290766374 · doi:10.1515/til-2022-0015

Bilateral labor agreements as migration governance tools: An analysis from a gender lens

2022· article· en· W4290766374 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.

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

Bibliographic record

VenueTheoretical Inquiries in Law · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Labor and Employment Law
Canadian institutionsWilfrid Laurier UniversityBalsillie School of International Affairs
Fundersnot available
KeywordsNormativeCorporate governanceContext (archaeology)Gender equalityWork (physics)Human rightsPolitical scienceInequalityGender inequalityGender diversitySociologyGender studiesEconomicsGeographyLawEngineeringManagement

Abstract

fetched live from OpenAlex

Abstract This Article discusses BLAs as tools of global labor migration governance, with a specific focus on gender. Drawing on our global database of 582 bilateral labor migration agreements (BLAs), we investigate the extent to which these governing instruments connect and align with relevant international normative frameworks, in particular the extent to which they represent gains, gaps or gaffs in terms of gender equality and the human and labor rights protection of women migrants. In the context of the Global Compact for Safe, Orderly and Regular Migration (GCM), which stresses a gender-responsive approach to migration governance as one of its guiding principles, we ask: Do the BLAs which are increasingly being used as instruments to govern labor migration contribute toward sustainable gender equality, decent work and reduced inequalities for women and gender-diverse migrants?

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.008
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0140.000

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 head0.343
Teacher spread0.302 · 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