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Record W4385768631 · doi:10.1080/13636820.2023.2246327

Perpetuating precarity in theory and in practice: a case study of work-integrated learning in the non-profit sector in Northern Canada

2023· article· en· W4385768631 on OpenAlexaffabout
Amelia F Merrick

Bibliographic record

VenueJournal of Vocational Education and Training · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
Fundersnot available
KeywordsPrecarityExperiential learningSociologyAgency (philosophy)Profit (economics)Public relationsPolitical scienceEconomicsSocial scienceGender studiesPedagogy

Abstract

fetched live from OpenAlex

Work-integrated learning (WIL) is a process of curricular experiential education within a workplace or practical setting. WIL is portrayed as a win-win, yet this research suggests that WIL perpetuates precarity and deepens inequalities between students, between different types of employers, and between geographic regions. Using the Human Development and Capabilities Approach (HDCA), this study investigated how eight diverse non-profit organisations (NPOs) in northern Canada are positioned to support students to develop personal agency through WIL. Most WIL research is urban-centric, focused on for-profit industries and framed within Human Capital Theory (HCT), making this study an outlier. Using a case study approach underpinned by critical and social realism, this study explores the ways in which WIL enables and constrains the development of agency at individual, social, and institutional levels. The research shows inconsistencies in current approaches to WIL. The increasingly precarious positioning of NPOs within the labour market threatens their ability to offer students (future) decent work. The institutional and policy environments that undergird WIL do not acknowledge the distinctness of non-profit organisations within a neoliberal economy and this makes invisible other dimensions that affect decent work, such as the regulatory environment, collectivisation, and the ‘contracting regime.’

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)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.584
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
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 head0.051
GPT teacher head0.400
Teacher spread0.350 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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