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Record W4402498922 · doi:10.1515/9780228022183

Forced Migration in/to Canada

2024· book· en· W4402498922 on OpenAlexaboutno aff

Bibliographic record

VenueMcGill-Queen's University Press eBooks · 2024
Typebook
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsForced migrationPolitical scienceGeographyGeologyArchaeologyRefugee

Abstract

fetched live from OpenAlex

Forced migration shaped the creation of Canada as a settler state and is a defining feature of our contemporary national and global contexts. Many people in Canada have direct or indirect experiences of refugee resettlement and protection, trafficking, and environmental displacement. Offering a comprehensive resource in the growing field of migration studies, Forced Migration in/to Canada is a critical primer from multiple disciplinary perspectives. Researchers, practitioners, and knowledge keepers draw on documentary evidence and analysis to foreground lived experiences of displacement and migration policies at the municipal, provincial, territorial, and federal levels. From the earliest instances of Indigenous displacement and settler colonialism, through Black enslavement, to statelessness, trafficking, and climate migration in today’s world, contributors show how migration, as a human phenomenon, is differentially shaped by intersecting identities and structures. Particularly novel are the specific insights into disability, race, class, social age, and gender identity. Situating Canada within broader international trends, norms, and structures – both today and historically – Forced Migration in/to Canada provides the tools we need to evaluate information we encounter in the news and from government officials, colleagues, and non-governmental organizations. It also proposes new areas for enquiry, discussion, research, advocacy, and action.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.058
Threshold uncertainty score0.424

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0180.006
Scholarly communication0.0050.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0140.001

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 head0.226
Teacher spread0.214 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueMcGill-Queen's University Press eBooks→Same topicMigration, Refugees, and Integration→French-language works237,207→