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An Auction Model of Canadian Temporary Immigration for the 21<sup>st</sup> Century

2008· article· en· W3121260456 on OpenAlexaffabout
Don J. DeVoretz

Bibliographic record

VenueInternational Migration · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsVoucherImmigrationGovernment (linguistics)Economic shortageThe InternetLabour economicsEconomicsBusinessPolitical scienceDemographic economicsLaw

Abstract

fetched live from OpenAlex

ABSTRACT Temporary Canadian immigration has grown beyond traditional programs for students, caregivers and agricultural workers to include trade‐related temporary visas under NAFTA and the GATS. Current Canadian debates envision supplanting Canada's permanent immigration programme with a temporary visa programme to remove skill shortages. Several questions emerge under these temporary schemes including who should choose the number of temporary immigrants and under what employment conditions. This paper offers an alternative policy to the current government‐determined quota on temporary visas to answer these two questions. Under the proposed scheme offered in this paper, a Canadian worker can place a job voucher up for auction on the Internet. If the Canadian worker finds an acceptable offer for his one‐year (or less) voucher, then the temporary immigrant is permitted to seek a job in Canada. Thus, under this auction scheme Canadian workers are compensated for the presence of temporary immigrants, and the actual number of temporary immigrants admitted depends on the total number of Canadian workers who sell their vouchers, not on a government fat.

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 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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

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

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 head0.028
GPT teacher head0.283
Teacher spread0.254 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations11
Published2008
Admission routes2
Has abstractyes

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