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Record W1580390636 · doi:10.22329/celt.v3i0.3246

16. Culture and Ethics in First Nations Educational Research

2010· article· en· W1580390636 on OpenAlexaffvenueabout
Josiah Taylor, Evie Plaice, Imelda Perley

Bibliographic record

VenueCollected Essays on Learning and Teaching · 2010
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsNexus (standard)PraxisEngineering ethicsSituatedSociologyConstruct (python library)Research ethicsEconomic JusticeRationalityEnvironmental ethicsPedagogyPublic relationsPolitical scienceLawEngineering

Abstract

fetched live from OpenAlex

In this paper, we share phenomena experienced by a multi-cultural research team working collaboratively with Wolastoq (Maliseet) First Nations Elders to document rapidly disappearing Wolastoq language, culture, and knowledge. This knowledge will ultimately be stored in databanks for future educational, community, and heritage use. Embedded within this research experience is a constantly evolving ebb and flow of culture, being, and relationships. As a collaborative research team, we explore ethical ramifications of dynamic, symbiotic relationships we share with Elder participants, requirements of university ethical review processes, and how this process shapes the knowledge that we collaboratively produce. We question how this nexus of cultures and ethics of researchers and collaborators directs the educational materials that we construct. Situated between the high tide of ethical standards and the low tide of the application of these ethics, is where the tides meet, and standards and praxis interact. Lastly, we suggest ways to supplement the ethics review process for social and educational research to better respect the individual rights and rationality of participants with whom we research, deepening the significance of such studies and promoting social justice.

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)0.003
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.665
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0250.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.009
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 head0.075
GPT teacher head0.457
Teacher spread0.382 · 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 designNot applicable
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

Citations2
Published2010
Admission routes3
Has abstractyes

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