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How Do Labor Market Networks Work?

2015· other· en· W1495791997 on OpenAlexaff
Brian Rubineau, Roberto M. Fernandez

Bibliographic record

VenueEmerging Trends in the Social and Behavioral Sciences · 2015
Typeother
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsMcGill University
Fundersnot available
KeywordsScrutinyMatching (statistics)Triad (sociology)Perspective (graphical)CertaintyWork (physics)Process (computing)MicroeconomicsEconomicsComputer scienceSociologyPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Abstract The informal seeking and sharing of job opportunity information via contacts are the dominant mechanisms for both the supply and demand sides of the labor market. Despite many decades of scholarly scrutiny, we have established little certainty about the mechanisms through which labor market networks operate. Much of this uncertainty results from single‐perspective investigations of a fundamentally triadic process. Network‐mediated job search is not merely a version of the classic two‐way matching problem with some additional network factors but is rather a three‐way matching problem with three distinct agentic decision makers: the job seeker, the job screener, and the social contact acting as a connector. This essay summarizes what is currently known about the operation and consequences of labor market networks, their mechanisms, and their contextual dependencies. We show how the perspective of a triad of actors presents new opportunities for resolving current contradictory empirical findings and areas of ongoing debate. Progress on this topic requires both careful causal research isolating mechanisms affecting a particular actor and integrative research on how these mechanisms interact among the triad of actors.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.239
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.002
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.079
GPT teacher head0.385
Teacher spread0.306 · 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.

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

Citations14
Published2015
Admission routes1
Has abstractyes

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