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Record W1592905220 · doi:10.7202/1069538ar

Can University/Community Collaboration Create Spaces for Aboriginal Reconciliation?

2020· article· en· W1592905220 on OpenAlexaffvenueabout
Ginette Lafrenière, Papa Lamine Diallo, Donna Dubie, Lou Henry

Bibliographic record

VenueFirst Peoples Child & Family Review An Interdisciplinary Journal Honouring the Voices Perspectives and Knowledges of First Peoples · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsWilfrid Laurier UniversityUniversité du Québec à Montréal
Fundersnot available
KeywordsScholarshipSociologyRestorative justiceEconomic JusticeOrder (exchange)Public relationsEngineering ethicsCriminologyPolitical scienceLawEngineering

Abstract

fetched live from OpenAlex

In this article, the authors attempt to illustrate how two Aboriginal community-based projects were conceptualized and developed through the collaborative efforts of four individuals who believed in the merits of a project aimed at survivors and intergenerational survivors of the residential school system as well as Aboriginal people in trouble with the law. Drawing upon a small body of literature on university/community collaboration, the authors illustrate the importance of meaningful collaboration between universities and communities in order to enhance a mutually beneficial relationship conducive to community-engaged scholarship. Through an examination of the case study of the Healing of The Seven Generations Project and the Four Directions Aboriginal Restorative Justice Project, the authors hope to illustrate to fellow Aboriginal colleagues in Canada the merits, strengths and challenges of university/ community collaboration. Ultimately, what the authors hope to share through this article is an example of how university/community collaboration can create spaces whereby Aboriginal people have become agents of their own healing.

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)0.022
metaresearch head score (Gemma)0.024
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: none
Teacher disagreement score0.985
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0160.022
Scholarly communication0.0180.018
Open science0.0030.023
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0100.001

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 head0.020
GPT teacher head0.325
Teacher spread0.305 · 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

Citations5
Published2020
Admission routes3
Has abstractyes

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