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Record W3014721620 · doi:10.33182/ml.v17i2.768

Mula Sa Masa, Tungo Sa Masa, From the People, To the People: Building Migrant Worker Power through Participatory Action Research

2020· article· en· W3014721620 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueMIGRATION LETTERS · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsYork University
Fundersnot available
KeywordsParticipatory action researchEthosSociologyImmigrationPower (physics)Citizen journalismAction (physics)ProductivityMigrant workersWork (physics)PoliticsGender studiesPolitical scienceEconomic growthLawAnthropologyEconomics

Abstract

fetched live from OpenAlex

In this article, we explore the possibilities of Participatory Action Research (PAR) producing ethical and nuanced knowledge that contributes to developing Filipino migrant workers’ capacity for sustainable political organizing. We discuss our projects with Filipino migrant organizations in the U.S. and Canada. We theorize on the potential of PAR with migrants who are part of highly precarious workforces in global cities. Additionally, we, as immigrant women of colour and scholars, highlight the tensions between academic ethos that prioritizes a rapid ‘publish-or-perish’ culture and the ethos of PAR, which puts into place collaborative processes that can be at odds with the ‘tempo’ of academic work. We highlight the tensions between the academic and reproductive labour of PAR, with the latter being seen by many academic institutions as an ‘inconvenience’ impeding productivity.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.449
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0030.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.172
GPT teacher head0.429
Teacher spread0.257 · 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