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Record W4220663646 · doi:10.1007/s12134-022-00951-4

Indigenous Perspectives of Immigration Policy in a Settler Country

2022· article· en· W4220663646 on OpenAlexafffundabout
Harald Bauder, Rebecca Breen

Bibliographic record

VenueJournal of International Migration and Integration / Revue de l integration et de la migration internationale · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsToronto Metropolitan University
FundersGovernment of Canada
KeywordsIndigenousImmigrationSovereigntyImmigration policyState (computer science)Westphalian sovereigntyPolitical sciencePolitical economySociologyColonialismGender studiesDevelopment economicsLawPoliticsEconomics

Abstract

fetched live from OpenAlex

Abstract The immigration policies in settler colonial countries rarely consider Indigenous perspectives or solicit their input—a reality that is particularly problematic given the key role that immigration policies have played and continue to play in the colonialization process. In this paper, we use Canada as a case study to examine the intersection of Indigenous experiences and the country’s immigration policy, and why and how Indigenous voices have been excluded from decision-making about immigrant selection. In addition, we review the academic and grey literature to investigate what the Indigenous perspectives that have been shared surrounding immigration policy currently are. Some perspectives affirm the need and desire for new immigrants while simultaneously engaging with the Canadian state’s problematic treatment of temporary migrants. Other perspectives fundamentally challenge the Westphalian state and its claim to regulate human mobility in the name of sovereignty. We connect these perspectives with academic open borders and no border debates.

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)0.005
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.368

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0310.035
Scholarly communication0.0110.002
Open science0.0010.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.010
GPT teacher head0.322
Teacher spread0.312 · 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

Citations15
Published2022
Admission routes3
Has abstractyes

Explore more

Same venueJournal of International Migration and Integration / Revue de l integration et de la migration internationale→Same topicIndigenous Health, Education, and Rights→French-language works237,207→