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Record W4388046586 · doi:10.1177/00221856231191259

The brave new world of unstable jobs hiding in plain sight: A reply to Murphy and Turner

2023· article· en· W4388046586 on OpenAlexaff
Xavier St‐Denis

Bibliographic record

VenueJournal of Industrial Relations · 2023
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsResearch CanadaInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsSightIndustrial relationsOrder (exchange)Work (physics)SociologyIndustrial RevolutionSet (abstract data type)EconomicsPolitical scienceLaw

Abstract

fetched live from OpenAlex

The article Employment stability and decent work published in the Journal of Industrial Relations by Murphy and Turner presents evidence forming the basis of a claim that job instability has not increased in Ireland between 1998 and 2021. This contrasts with a rich literature in industrial relations and the sociology of work and organizations, which documents the fundamental transformation of employment relationships since the 1990s toward greater insecurity. In this response paper, I question the empirical foundations of Murphy and Turner's claims. Analyzing the same data set used in their study, I provide clear evidence that an increase in job instability consistent with the precarious work literature has been hiding in plain sight. I also engage with their efforts at theorizing the nature of the recent transformation of employment relationships in Ireland specifically, and in Liberal Market Economies more broadly. In doing so, I suggest research avenues that go beyond a polarized debate in whether or not job instability has increased in order to contribute to a more complex understanding of contemporary changes in career trajectories.

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)0.021
metaresearch head score (Gemma)0.074
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.030
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.074
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0060.027
Scholarly communication0.0110.025
Open science0.0040.007
Research integrity0.0300.050
Insufficient payload (model declined to judge)0.0030.002

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 head0.146
GPT teacher head0.407
Teacher spread0.261 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations1
Published2023
Admission routes1
Has abstractyes

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