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Record W4386195832 · doi:10.1111/cars.12450

Mr Speaker: The changing nature of parliamentary debates on immigration in Canada

2023· article· en· W4386195832 on OpenAlexafffundabout
Ravi Pendakur, Sabrina Sarna

Bibliographic record

VenueCanadian Review of Sociology/Revue canadienne de sociologie · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHouse of CommonsAllianceImmigrationPoliticsPolitical sciencePolitical economyConservatismDemocracyLiberal PartyRhetoricImmigration policyLawPublic administrationSociology

Abstract

fetched live from OpenAlex

We use correspondence analysis to look at the changing nature of political debates in the Canadian House of Commons concerning immigration over a five-decade period. Using data drawn from the Linked Parliamentary Data (LiPaD) project we assess the way in which immigration policy and issues are discussed by different political parties from September of 1968 to June of 2019. We look at debates in five of the longest Prime Ministers' mandates. In doing so, we trace changes in both emphasis and rhetoric by political party. We find that party political views on immigration became more polarized with the breakup of the Progressive Conservative party in the early 1990s. Liberal party views moved toward to the left of the spectrum while the Reform/Alliance/Conservative Party of Canada parties moved toward the right and became increasingly entrenched until 2015. After that, the Conservatives and the Liberals moved closer to the centre. The New Democratic Party was the most consistent in its views over time, focusing on issues of humanitarianism as well as broad policy issues.

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)0.006
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.751

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0070.015
Science and technology studies0.0150.005
Scholarly communication0.0070.001
Open science0.0020.003
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 head0.045
GPT teacher head0.310
Teacher spread0.265 · 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 designQualitative
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

Citations3
Published2023
Admission routes3
Has abstractyes

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