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Record W3161614496

Basic Information and Communication Technology Skills among Canadian Immigrants and Non-Immigrants

2018· article· en· W3161614496 on OpenAlexafffundabout
N.T. Khuong Truong, Arthur Sweetman

Bibliographic record

VenueRePEc: Research Papers in Economics · 2018
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsMcMaster University
FundersOntario Ministry of Health and Long-Term Care
KeywordsImmigrationInformation and Communications TechnologyEarningsDemographic economicsBusinessEconomicsPolitical scienceAccounting
DOInot available

Abstract

fetched live from OpenAlex

Male immigrants are observed to be disproportionately employed in ICT information and communication technology (ICT) industries and occupations. A measure of basic ICT skills is employed to document differences in skill levels and labour market earnings across immigration classes and categories of Canadians at birth. Adult immigrants, including those assessed by the points system, are found to have lower average ICT scores than Canadians at birth, although the rate of return to ICT skills is not statistically different between them. Immigrants who arrive as children, and the Canadian-born children of immigrants, have similar outcomes to the Canadian-born children of Canadian-born parents.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.244
Teacher spread0.237 · 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
Published2018
Admission routes3
Has abstractyes

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