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Record W1629982861

Between conformity and contestation: South Asian immigrant women negotiating soft skill training in Canada

2015· article· en· W1629982861 on OpenAlexaffabout
Srabani Maitra

Bibliographic record

VenueENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsYork University
Fundersnot available
KeywordsSoft skillsImmigrationAgency (philosophy)NormativeContext (archaeology)NegotiationSociologyTraining (meteorology)Gender studiesLabour economicsPolitical sciencePsychologySocial psychologyEconomicsSocial scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

In the current Canadian neoliberal labour market, work-related learning and training are considered key strategies for developing workers’ economic productivity as well as expediting their integration to the labour market. An important aspect of such training and learning now consists of soft skills. Yet some scholars are ambivalent about the nature of such soft skill training as their curriculum are often suffused with cultural and racial values geared towards assimilating immigrants of colour to the dominant and normative national culture of the country. This paper further problematizes soft skill training by examining the training/learning experiences of highly educated South Asian women trying to enter the Canadian labour market after immigration. In particular, it highlights these women’s engagement with such soft skill training and their negotiation processes, thereby analyzing their agency in the context of work-related learning.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.052
Threshold uncertainty score0.379

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0410.020
Scholarly communication0.0090.002
Open science0.0020.008
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.000

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 head0.292
Teacher spread0.248 · 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

Citations16
Published2015
Admission routes2
Has abstractyes

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