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Record W4235137484 · doi:10.32920/14638707

Gaining Institutional Permission: Researching Precarious Legal Status in Canada

2021· preprint· en· W4235137484 on OpenAlexaffabout
Judith K. Bernhard, Julie E. E. Young

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsYork UniversityToronto Metropolitan University
Fundersnot available
KeywordsConfidentialityDeportationVulnerability (computing)RefugeePolitical scienceNegotiationAllianceLaw enforcementEnforcementImmigrationLegal processPublic relationsLawCriminologySociologyComputer security

Abstract

fetched live from OpenAlex

There is limited research into the situations of people living with precarious status in Canada, which includes people whose legal status is in-process, undocumented, or unauthorized, many of whom entered the country with a temporary resident visa, through family sponsorship arrangements, or as refugee claimants. In 2005, a community-university alliance sought to carry out a research study of the lived experiences of people living with precarious status. In this paper, we describe our negotiation of the ethics review process at a Canadian university and the ethical, legal, and methodological issues that emerged. Although being able to guarantee our participants complete confidentiality was essential to the viability of the project due to their vulnerability to detention or deportation, we discovered that the Canadian legal framework limited us to being able to offer them confidentiality “to the fullest extent possible by law.” One way to overcome this conflict would have been through the construction of a Wigmore defence, in which we would document that the research would not be possible without assurance of our participants’ confidentiality. Such a defence would be tested in court if our research records were subpoenaed by immigration enforcement authorities. Rather than take the risk that this defence would not be successful and would result in our participants being deported, we altered the research methods from using multiple interviews to establish trust (which would have required that we store participants’ contact information) to meeting participants only once to discuss their experiences of living with precarious legal status in Canada. Our encounter with the ‘myth of confidentiality’ raised questions about the policing of knowledge production.

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)0.015
metaresearch head score (Gemma)0.035
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 score0.160
Threshold uncertainty score0.974

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.035
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.008
Science and technology studies0.0460.024
Scholarly communication0.0130.005
Open science0.0050.012
Research integrity0.0020.007
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 head0.058
GPT teacher head0.376
Teacher spread0.317 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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