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Record W7095175550

Immigrants: Settling for less?

2014· article· en· W7095175550 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationUnemploymentJob lossUnemployment rateDegree (music)
DOInot available

Abstract

fetched live from OpenAlex

During the 1990s, immigration policyfavoured the admission of immigrants withhigher education, leading to a significant increase in the education level of recent immigrants (see Definitions). In 2001, more than 40 % of recent immigrants had at least a bachelor’s degree, compared with 22 % in 1991. As a consequence, they accounted for 6 % of all persons in Canada with a university degree in 2001, up from 4 % in 1991. One of the first hurdles for immigrants is finding a job in an unfamiliar labour market. Among persons aged 25 to 54 with a university degree, unemployment for recent immigrants has consistently been at least tri-ple the rate for the Canadian-born—in 2001, the rates were 7.4 % versus 2.3 % for men and 10.5 % versus 2.7 % for women. The difficulty recent immigrants face in finding a job has been attributed to several factors:

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)0.002
metaresearch head score (Gemma)0.005
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: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.002
Scholarly communication0.0040.004
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0270.003

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 head0.315
Teacher spread0.287 · 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
Published2014
Admission routes1
Has abstractyes

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