MétaCan
Menu
Back to cohort
Record W4316014696 · doi:10.1080/07360932.2022.2164039

How to Get Punched by the ‘Weak’: <i>An Analysis of the Agency of Filipina Domestic Workers in a Global, Unequal, and Gendered Labor System</i>

2023· article· en· W4316014696 on OpenAlex
Jaron Chalier

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

VenueForum for Social Economics · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsCarleton University
Fundersnot available
KeywordsLeverage (statistics)NarrativeCitizenshipPunchingPower (physics)Political scienceSociologyGender studiesPublic relationsLawComputer scienceEngineeringPoliticsArtificial intelligence

Abstract

fetched live from OpenAlex

Studying the challenges, suffering, and exploitation of the underprivileged around the world becomes valuable when it allows us to identify and then fix a problem. This paper pushes back against a narrative characterizing Filipina domestic workers as unable to fight against the global, unbalanced, sexualized, racialized, gender regime they work under. There are many examples where they are able to make a difference without needing the same resources as large international organizations or states. The key findings are a list of 10 actions taken by them, summarized as a tried and tested, non-exhaustive list of tools of the underprivileged ‘punching’ back. Using citizenship to leverage governments’ power, using their voices to leverage NGOs, reconciling and finding benefit with pervasive judgment and stereotypes, and even creating entire NGOs, magazines, and social movements, are all examples of how supposedly weak victims punch back.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score0.899

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 studies0.0000.000
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 head0.020
GPT teacher head0.295
Teacher spread0.275 · 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