MétaCan
Menu
Back to cohort
Record W4213361891 · doi:10.32920/ryerson.14647908.v1

From the Komagata Maru to six Sikh MPs in Parliament : factors influencing electoral political participation in the Canadian-Sikh community

2021· preprint· en· W4213361891 on OpenAlexaboutno aff
Geetika Singh Bagga

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicSouth Asian Studies and Diaspora
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsParliamentRepresentation (politics)Inclusion (mineral)ImmigrationInterpretation (philosophy)Political scienceSociologyPublic administrationGender studiesPublic relationsLaw

Abstract

fetched live from OpenAlex

In the span of about 100 years Canadian-Sikhs have negotiated the path from political exclusion to political inclusion. This ethno-specific study focuses on the federal and provincial electoral performance of the Canadian-Sikh community and attempts to answer the following research questions. What factors have contributed to these electoral achievements, and what does having representation mean for the Sikh community -- have the results been substantive or symbolic? The research discuses [sic] a range of internal (community specific) and external (structural) factors that have allowed for such electoral success; and utilizes key informant interviews and a political engagement survey to examine community motivations for having elected representation. The study concludes by raising questions about the traditional interpretation around the nature of immigrant integration and political participation. Also highlighted here is the need for further ethnospecific research that recognizes the complexity of the relationship between the factors that influence political participation across different communities.

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)0.004
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 score0.152
Threshold uncertainty score0.306

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.003
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.001
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 head0.105
GPT teacher head0.306
Teacher spread0.201 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicSouth Asian Studies and DiasporaFrench-language works237,207