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Record W2518186030 · doi:10.3138/cpp.2014-070

Targeting Tax Relief at Youth Employment

2016· article· en· W2518186030 on OpenAlexaffvenueabout
Matthew D. Webb, Arthur Sweetman, Casey Warman

Bibliographic record

VenueCanadian Public Policy · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsCarleton University
Fundersnot available
KeywordsSubsidyEarningsUnemploymentLabour economicsYouth unemploymentDemographic economicsEconomicsDisplacement (psychology)Unemployment rateDifference in differencesEconomic growthPsychologyFinance

Abstract

fetched live from OpenAlex

Canada's Youth Hires program was a targeted employment subsidy that rebated employment insurance premiums to employers with net increases in insurable earnings for youths aged 18–24 years. Using a difference-in-differences approach, in each of two data sets statistically and economically significant employment impacts are observed. Most of the evidence suggests that the 2–2.4 weeks of increased employment resulted from an aggregate reduction in those not in the labour force, with at most a modest change in the unemployment rate. Many estimated effects are larger for men than for women. Notably, strong evidence of displacement (substitution away from slightly older non-subsidized workers) is not observed. However, there may be a small reduction in full-time schooling for the targeted group.

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)0.002
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: Empirical
Teacher disagreement score0.876
Threshold uncertainty score0.250

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.029
GPT teacher head0.267
Teacher spread0.239 · 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

Citations2
Published2016
Admission routes3
Has abstractyes

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