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Record W1485133348 · doi:10.1515/bejeap-2015-0125

Love of Variety and Immigration

2016· article· en· W1485133348 on OpenAlexaff
Dhimitri Qirjo

Bibliographic record

VenueThe B E Journal of Economic Analysis & Policy · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsImmigrationMonopolistic competitionVariety (cybernetics)EconomicsDemocracyPoliticsCapital (architecture)TasteLiberalizationImmigration policyOrder (exchange)Labour economicsMarket economyPolitical scienceMonopolyLaw

Abstract

fetched live from OpenAlex

Abstract This paper develops a political-economic analysis of immigration in a host country that operates in a direct democracy regime. It shows that, in a monopolistic competitive environment with differentiated capital intensive goods, labor liberalization is more likely to come about in the societies that have more taste for variety. Moreover, in a host country with a strong preference for variety, workers and capital owners may share the same positive stance toward labor liberalization. It follows that the latter is impossible in a perfect competitive environment. Finally, in a dynamic inter-temporal setting with strategic voters, it demonstrates that the median voter is willing to accept fewer immigrants in the first period, in order to preserve her domestic political influence in the next period because of the naturalization of immigrants. In this way, the median voter maximizes her gains from immigration by accepting more immigrants in total.

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)0.003
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: none
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.000

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 head0.031
GPT teacher head0.230
Teacher spread0.200 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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