MétaCan
Menu
← Back to cohort
Record W2619450917

Immigration, Employment and Social Expenditures in Canadian Public Policy: Redistributive or Regulatory?

2016· article· en· W2619450917 on OpenAlexaffabout
Shamsuddin Ahmed

Bibliographic record

VenueSSRN Electronic Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsYork University
Fundersnot available
KeywordsImmigrationContext (archaeology)Diversity (politics)Demographic economicsPopulationSettlement (finance)Government (linguistics)Public policyGeographyPolitical scienceDevelopment economicsEconomicsEconomic growthSociologyDemographyLaw
DOInot available

Abstract

fetched live from OpenAlex

This research paper seeks to synthesise the queries whether the trends of immigration, employment, and social expenditures are a regulatory or redistributive pattern in Canada. Four fundamental and relational issues are explored. The annual inflow of immigrants and the persistent employment of active labours appear to be a conventional relationship with social expenditures as a grounded theory what is noticeable from the federal government’s historical records and moderately to employment versus population dispersions at the local context. Statistical results of the past three consecutive decades indicate that the key societal values such as the number of population and the scope of employment, apart from the association of social expenditure, standardise the dispersion and displacement of immigration influx. Analysis of regional spatial data indicates that highly populated areas, settlement types, and dwelling values chiefly normalise both the magnitude and the diversity of Canadian immigrants and delineate the patterns in a regional population that ultimately regulate the employments and social expenditures.

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)0.003
metaresearch head score (Gemma)0.009
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 score0.159
Threshold uncertainty score0.976

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.010
Science and technology studies0.0080.010
Scholarly communication0.0090.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.011
GPT teacher head0.285
Teacher spread0.274 · 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 routes2
Has abstractyes

Explore more

Same venueSSRN Electronic Journal→Same topicMigration and Labor Dynamics→French-language works237,207→