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Tipping points: what participants found valuable in labour market training programmes for vulnerable groups

2009· article· en· W1485850867 on OpenAlexaffabout
John R. Graham, Marion E. Jones, Micheal L. Shier

Bibliographic record

VenueInternational Journal of Social Welfare · 2009
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of ReginaUniversity of Calgary
Fundersnot available
KeywordsWelfareHuman capitalSocial capitalPsychologyPublic relationsPolitical scienceSociologyEconomic growthEconomicsSocial scienceLaw

Abstract

fetched live from OpenAlex

Graham JR, Jones ME, Shier M. Tipping points: what participants found valuable in labour market training programmes for vulnerable groups Int J Soc Welfare 2010: 19: 63–72 © 2009 The Author(s), Journal compilation © 2009 Blackwell Publishing Ltd and the International Journal of Social Welfare. This article is based on face‐to‐face and focus group interviews with 72 people who have experienced ongoing difficulties integrating into Canadian labour markets, and who had completed a labour market training programme. Participants were representative of at least one (and often several) categories that inhibited labour market integration: low socio‐economic status, Aboriginal status, single parenthood, criminal justice history and being disabled. The major finding: respondents associated life skills rather than labour‐market skills with success in overcoming personal barriers to securing and maintaining employment. Life skills involved developing life meaning and interpersonal skills related to personal cognition and behaviours. Valued cognitions identified by participants were gaining a new perspective on life and realising that the past impacts the present. Valued behaviours identified by participants included actions associated with understanding personal characteristics and motivations, and building positive social support and social capital. These insights provide theoretically rich considerations for labour market training programmes and could considerably influence labour market policies and practices, particularly since most training programmes and policies are geared toward human capital (i.e. labour‐market skills) accumulation.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.103
GPT teacher head0.450
Teacher spread0.346 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations22
Published2009
Admission routes2
Has abstractyes

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