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Record W7395875

Barriers to employment and utilization of highly skilled Iranians in Greater Vancouver Regional District

2007· dissertation· en· W7395875 on OpenAlexaboutno aff
Leyla Nouhi

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyDemographic economicsPolitical scienceSocioeconomicsSociologyEconomics
DOInot available

Abstract

fetched live from OpenAlex

To investigate barriers to employment and utilization of highly skilled Iranians in Greater Vancouver Regional District, I conducted a survey where the majority of respondents cited lack of Canadian work experience, lack of networking, lack of credential recognition, and lack of available jobs as their main obstacles to finding employment in their area of training.A large percentage of respondents did not obtain any information about the Canadian labour market prior to immigration.Of those who did so, most considered the information they accessed only 'somewhat helpful'.The objective of this study is to propose methods that will help smooth the transition of skilled Iranian-born immigrants and improve their integration into the labour market.Proposed alternatives to the status quo include the provision of mandatory information sessions prior to immigration, streamlining credential assessment, and developing internship programs to help skilled immigrants gain their first Canadian work experience.Promote existing credential recognition services to raise employer awareness about available services.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.449
Threshold uncertainty score0.904

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.028
GPT teacher head0.321
Teacher spread0.293 · 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
Published2007
Admission routes1
Has abstractyes

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