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Record W2900427887 · doi:10.29173/cjfy29392

“It Makes Me Feel Good to Teach People About My Culture:” On Collaborative Research Methods with Indigenous Young People

2018· article· en· W2900427887 on OpenAlexvenueaboutno aff
Amy Mack, Jan Newberry

Bibliographic record

VenueCanadian Journal of Family and Youth / Le Journal Canadien de Famille et de la Jeunesse · 2018
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousStudioSociologyIntervention (counseling)Space (punctuation)Vulnerability (computing)Media studiesPublic relationsPolitical scienceVisual artsMedicineArtEcologyNursing

Abstract

fetched live from OpenAlex

In this article we, as settler scholars, explore process as method within a community-driven, supradisciplinary project in southern Alberta called Raising Spirit. The project was a collaboration between the University of Lethbridge’s Institute for Child and Youth Studies and Opokaa’sin Early Intervention Society, a nonprofit that serves Indigenous children and families in southern Alberta. The project team formed in response to Opokaa’sin’s need for a digital library of Blackfoot culture, language, and history. Here, we reflect on the methods used during this project, specifically paraethnography (Marcus & Holmes, 2008) and design studio (Rabinow, Marcus, Faubion, & Rees, 2008). Throughout, we argue that this approach produced a collective sphere (Rappaport, 2008) wherein young people and community partners, Indigenous and non-Indigenous, became collaborators throughout the process. In this space of vulnerability and potential, everyone could contribute, share, and learn.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.350
Teacher spread0.321 · 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 teacher head, not a consensus.

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
Published2018
Admission routes2
Has abstractyes

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