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Record W4223427370 · doi:10.3167/arms.2022.050103

Forced-Voluntary Return

2022· article· en· W4223427370 on OpenAlexaboutno aff
Tanya Aberman

Bibliographic record

VenueMigration and Society · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsForced migrationRefugeeTurnoverPolitical scienceGovernment (linguistics)ChoseSociologyGender studiesDemographic economicsEconomicsLawManagement

Abstract

fetched live from OpenAlex

During the near decade of Conservative rule in Canada from 2006 to 2015, anti-refugee and anti-migrant discourse was continuously circulated by government officials. Social, economic, and physical restrictions were implemented based on the dichotomy of “deserving” versus “undeserving” migrants, and borders were created within communities. This article takes an intersectional approach to explore the reasons that some migrants chose to leave Canada “voluntarily” during that time, and the factors that forced them to do so. I offer the concept of forced-voluntary return to capture some of the tensions and messiness within migrant experiences that are neither completely voluntary nor forced. These tensions affirm the emerging calls in research to conceptualize migration on a spectrum from forced to voluntary, and contribute to understandings of migration management, the production of deportability, and the “voluntary” mobility of migrants by highlighting some of the ways in which intersecting identities impact migrants’ decisions about return.

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)0.003
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.492
Threshold uncertainty score0.977

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.020
Scholarly communication0.0050.002
Open science0.0010.007
Research integrity0.0010.003
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 head0.013
GPT teacher head0.269
Teacher spread0.256 · 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
Published2022
Admission routes1
Has abstractyes

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