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Record W3041482523

Routine Tasks were Demanded from Workers during an Energy Boom

2020· preprint· en· W3041482523 on OpenAlexaboutno aff
Joseph Marchand

Bibliographic record

VenueRePEc: Research Papers in Economics · 2020
Typepreprint
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsBoomEarningsCraftLabour economicsShock (circulatory)EconomicsEnergy (signal processing)BusinessDemographic economicsEngineeringFinanceMedicine
DOInot available

Abstract

fetched live from OpenAlex

Energy booms are most often associated with large increases in employment and earnings, as well as positive local labor market spillovers from energy to non-energy industries. In this study, the large, localized, and positive labor demand shock from an energy price boom in Western Canada was also found to increase the routine and manual task content of employment across the occupational distribution. Both occupation groups involving routine manual tasks (operators, fabricators and laborers; and production, craft, and repair), as well as one occupational group involving non-routine cognitive tasks (technicians), significantly increased their employment shares during this boom. However, these results show that only the routinization of employment had a significant impact on wages; not manualization. This conventional boom evidence illustrates how an energy boom can impact labor, beyond the traditional changes in employment and earnings, and serves as a counterexample to the documented occupational polarization often attributed to technological change.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.266
Threshold uncertainty score0.529

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.027
GPT teacher head0.271
Teacher spread0.244 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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Same venueRePEc: Research Papers in EconomicsSame topicEnergy and Environment ImpactsFrench-language works237,207