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Record W4400209924 · doi:10.4324/9781003499787-19

The Immigrant Policies of Canada and Racism

2024· book-chapter· en· W4400209924 on OpenAlexaboutno aff
Sajaudeen Chapparban

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationRacismPolitical scienceSociologyCriminologyHistoryGender studiesLaw

Abstract

fetched live from OpenAlex

The history of Indian migration to Canada goes back to the colonial enterprise of the mid- and late-19th century. Indians in Canada comprise the second-largest Asian and non-white community after the Chinese and have a unique experience of being, becoming, and belonging. Being the colonial subject of the British Empire, during the reign of Queen Victoria (1858), Indians were supposed to enshrine equal rights throughout the Empire but that did not work in practice because of the existing racial inequalities. Indians were subject to various discriminatory practices because of their racial and cultural differences in Canadian society. Starting from the colonial exclusionary immigrant policies to contemporary post/Multicultural policies shaped the psyches and experiences of assimilation and exclusion of the Indian community from mainstream white Canadian society. The present postcolonial reading critically analyses a brief history of the immigrant policies of Canada and how those policies affected the migration of non-white to Canada with special reference to the Indian diaspora. It also underscores how race played a vital role in engineering immigrant policies to exclude certain nationalities till the recent past because they did not look like the Canadian, the white. It also critiques the reprehensible reasons behind the introduction of Multiculturalism as state policies and immigrant policies that have shaped the Indians’ and other non-white communities’ experiences in Canadian society. And amidst these “ideal policies” how did the racist sentiments and leadership negotiate and function, which further contributed to the sparks of the racist attacks and discriminations against the coloured Canadians/immigrants in the post-multicultural Canadian society?

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)0.002
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: none
Teacher disagreement score0.183
Threshold uncertainty score0.948

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0470.022
Scholarly communication0.0100.002
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.008
GPT teacher head0.243
Teacher spread0.235 · 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
Published2024
Admission routes1
Has abstractyes

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