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Record W3027034729 · doi:10.1177/1060826520921194

Passion in a Climate of Austerity: Young Men’s Perceptions of Education and Career Success in a Polarized Economy

2020· article· en· W3027034729 on OpenAlexafffundabout
Nitin Deckha

Bibliographic record

VenueThe Journal of Men s Studies · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsUniversity of Guelph-Humber
FundersUniversity of Guelph
KeywordsPassionAusterityContext (archaeology)Construct (python library)EntrepreneurshipSociologyPolitical scienceGender studiesPsychologySocial psychologyPolitics

Abstract

fetched live from OpenAlex

This article explores young men’s educational experiences and career trajectories in the context of restrained public expenditure, neoliberal educational policies, tightening job opportunities, and growing concern of the gender achievement gap. Based on focus group research among young postsecondary-educated students in Ontario, Canada, this article reveals how young men, in particular, emphasize the importance of passion and purpose in creating successful selves and in navigating higher education. The author examines research findings through a transdisciplinary lens that juxtaposes psychological research on passion, management perspectives on success, economic studies on gender and the labor market, and critical perspectives of gendered subjectivities within the context of a declining manufacturing sector and a rising service-led knowledge economy to explore and analyze how young men construct their learner subjectivities. As such, these narratives should be read as the product of risk-taking, heroic, and self-confident self-entrepreneurship that necessarily involves self-regulation, introspection, diligence, and responsibility.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.142
Threshold uncertainty score0.183

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.036
GPT teacher head0.285
Teacher spread0.249 · 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

Citations0
Published2020
Admission routes3
Has abstractyes

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