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Record W1996603074 · doi:10.1080/09638199.2011.558629

Skilled–unskilled wage inequality and imitation in a product variety model: A theoretical analysis

2011· article· en· W1996603074 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of International Trade & Economic Development · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsnot available
FundersIndian Statistical InstituteMemorial University of Newfoundland
KeywordsEconomicsImitationLabour economicsWageWelfareProduct (mathematics)EndowmentWage inequalityVariety (cybernetics)Market economy

Abstract

fetched live from OpenAlex

The article develops a dynamic three-sector product variety model to analyze the role of imitation on skilled–unskilled wage inequality. One of these sectors produces varieties of innovated products with skilled labor as well as unskilled labor and another sector produces varieties of imitated products with only unskilled labor. Also, there is an R&D sector developing blueprints of new products with skilled labor as the only input. However, imitation is costless. It is shown that an increase in skilled (unskilled) labor endowment raises (lowers) the rate of growth, raises (lowers) the skilled–unskilled wage ratio, and lowers (raises) the level of social welfare. However, an increase in the rate of imitation raises this growth rate, lowers the skilled–unskilled wage ratio, and raises the level of social welfare.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.306
Threshold uncertainty score0.743

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.040
GPT teacher head0.238
Teacher spread0.198 · 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