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Record W4311917512 · doi:10.32920/21753401.v1

Beyond Recovery: Colonization, Health and Healing for Indigenous People in Canada

2022· preprint· en· W4311917512 on OpenAlexaboutno aff
Lynn F Lavallée, Jennifer Poole

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousMental healthIdentity (music)Historical traumaPoliticsColonizationPolitical scienceSociologyMedicineGender studiesNursingHistoryPsychiatryAestheticsLawArtArchaeology

Abstract

fetched live from OpenAlex

How do we limit our focus to mental health when Indigenous teaching demands a much wider lens? How do we respond to mental health recovery when Indigenous experience speaks to a very different approach to healing, and how can we take up the health of Indigenous people in Canada without a discussion of identity and colonization? We cannot, for the mental health and recovery of Indigenous people in Canada have always been tied to history, identity, politics, language and dislocation. Thus, in this paper, our aim is to make clear that history, highlight the impacts of colonization and expound on Indigenous healing practices taking place in Toronto. Based on findings from a local research project, we argue these healing practices go beyond limited notions of recovery and practice, offering profound and practical ways to address the physical, emotional, spiritual and mental health of Indigenous peoples.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.598

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 studies0.0320.014
Scholarly communication0.0050.002
Open science0.0010.006
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 head0.020
GPT teacher head0.358
Teacher spread0.338 · 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

Citations32
Published2022
Admission routes1
Has abstractyes

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