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Intermediaries

2017· other· en· W4240924469 on OpenAlexaff
Sarah Zell

Bibliographic record

VenueInternational Encyclopedia of Geography · 2017
Typeother
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIntermediaryRestructuringLabour economicsAgency (philosophy)BusinessLabor market segmentationDivision of labourSecondary labor marketLabor relationsMatching (statistics)Market economyEconomicsWageMarketingFinance

Abstract

fetched live from OpenAlex

Labor market intermediaries act as brokers, facilitating job matching between employers and jobseekers. Intermediaries such as temporary employment agencies have expanded rapidly under neoliberal economic restructuring. They benefit from a demand for flexible labor and also promote it by providing just‐in‐time labor. Intermediaries contribute to the social regulation and segmentation of labor markets, often reinforcing inequalities in labor market access. Their practices impact labor mobility and agency, and they present challenges to labor organizing. Recently, intermediaries have expanded into new global markets and are playing an increasingly significant role in facilitating transnational labor migration.

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)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.722
Threshold uncertainty score0.930

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0050.003
Scholarly communication0.0120.009
Open science0.0030.010
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.2780.112

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 head0.020
GPT teacher head0.371
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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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