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Record W3123267736 · doi:10.1257/000282804322970779

Internet Job Search and Unemployment Durations

2004· article· en· W3123267736 on OpenAlexaff
Peter Kuhn, Mikal Skuterud

Bibliographic record

VenueAmerican Economic Review · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Market Behavior and Pricing
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsUnemploymentThe InternetWork (physics)EconomicsLabour economicsDifferential (mechanical device)Demographic economicsInternet usersComputer scienceWorld Wide WebEngineeringEconomic growth

Abstract

fetched live from OpenAlex

Using the December 1998 and August 2000 CPS Computer and Internet Supplements matched with subsequent CPS files, we ask which types of unemployed workers looked for work on line and whether Internet searchers became reemployed more quickly. In our data, Internet searchers have observed characteristics that are typically associated with shorter unemployment spells, and do spend less time unemployed. This unemployment differential is however eliminated and in some cases reversed when we hold observable characteristics constant. We conclude that either Internet job search is ineffective in reducing unemployment durations, or Internet job searchers are negatively selected on unobservables.

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)0.001
metaresearch head score (Gemma)0.010
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.279
Teacher spread0.243 · 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

Citations369
Published2004
Admission routes1
Has abstractyes

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