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Record W3093178083 · doi:10.1111/imig.12769

The Construction of Gratitude in the workplace: Temporary foreign workers employed in health care

2020· article· en· W3093178083 on OpenAlexaffabout
Shiva Nourpanah

Bibliographic record

VenueInternational Migration · 2020
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of GuelphSaint Mary's University
Fundersnot available
KeywordsGratitudeFeelingPoliticsSociologyWork (physics)Health carePolitical sciencePolitical economyPublic relationsSocial psychologyPsychologyLaw

Abstract

fetched live from OpenAlex

Abstract In capitalist societies, workers feel, or feel obliged to feel grateful for having a job. In a world marked by global inequality, migrants from the global South to the North are expected to feel gratitude for their opportunity to move to and live in a first world country. In the case of temporary foreign workers, both these sorts of gratitude come together. The political economy regime governing the conditions of work and cross‐border movement for temporary foreign workers employed in health care in Canada engenders feelings of gratefulness from workers towards their employers. Contextualized within a cross‐disciplinary study of gratitude as a social construct, this article uses the case of this particular sort of work and migration gratitude to develop knowledge on the nuanced and complex ways in which structures of feeling lead from and loop back into capitalist political economies.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.015
Scholarly communication0.0060.003
Open science0.0010.008
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.386
Teacher spread0.342 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2020
Admission routes2
Has abstractyes

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