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Record W2616632524 · doi:10.1093/esr/jcy045

Contingent Employment and Labour Market Pathways: Bridge or Trap?

2018· article· en· W2616632524 on OpenAlexfundno aff
Duncan McVicar, Mark Wooden, Inga Laß, Yin-King Fok

Bibliographic record

VenueEuropean Sociological Review · 2018
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
FundersQueen's UniversityDepartment of Social Services, Australian GovernmentAustralian Research CouncilQueen's University BelfastAustralian Government
KeywordsCasualTrap (plumbing)Bridge (graph theory)EconomicsLabour economicsDemographic economicsPolitical scienceBiologyGeography

Abstract

fetched live from OpenAlex

The debate over whether contingent (and typically more precarious) employment acts as a bridge to permanent employment, or as a trap, has tended to focus on transitions rather than longer-run pathways. This approach cannot accurately identify indirect pathways from contingent to permanent employment or ‘trap’ pathways involving short spells in other states. It also fails to distinguish between those experiencing contingent employment as a ‘blip’ and those with longer spells. This article employs a different approach involving sequence analysis. Exploiting longitudinal data for Australia, evidence for the co-existence of pathways that correspond to ‘bridge’ and ‘trap’ characterizations of contingent employment is found. Further, in the case of casual employment—the most common form of contingent employment in Australia—these two types of labour market pathways are roughly equally prevalent, although for women and those with low educational attainment ‘traps’ are more likely than ‘bridges’.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.004
Scholarly communication0.0020.006
Open science0.0010.004
Research integrity0.0010.002
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 head0.224
GPT teacher head0.436
Teacher spread0.212 · 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 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

Citations30
Published2018
Admission routes1
Has abstractyes

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Same venueEuropean Sociological ReviewSame topicEmployment and Welfare StudiesFrench-language works237,207