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Record W4321111951 · doi:10.4018/ijmbl.318262

Nisotak

2023· article· en· W4321111951 on OpenAlexafffundabout
Marguerite Koole, Randy Morin, Kevin wâsakâyâsiw Lewis, Kristine Dreaver‐Charles, Ralph Deters, Julita Vassileva, Frank B. W. Lewis

Bibliographic record

VenueInternational Journal of Mobile and Blended Learning · 2023
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsUniversity of Saskatchewan
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Saskatchewan
KeywordsIndigenousUsabilityHonourComputer scienceInterface (matter)World Wide WebSpace (punctuation)Indigenous languageUser interfaceKnowledge managementHuman–computer interactionPolitical science

Abstract

fetched live from OpenAlex

This paper outlines the design, development, and preliminary usability study of a system comprising 1) a web-based Indigenous lesson-creation interface and 2) an accompanying mobile app for studying the lessons. The Nisotak project was developed in response to the need for the preservation of Indigenous languages and to support reconciliation within Canada. In this paper, the authors discuss the technological aspects of the project and the less tangible decision-making that helped navigate software development in ways that support and honour Indigenous languages, Indigenous knowledge, and Indigenous people while, at the same time, making space for non-Indigenous allies. The key decisions that guided this project included privileging the target language(s), accommodating multiple dialects, creating an easy-to-use and engaging interface for non-technical users, and designing for easy transfer of ownership and management. Finally, the authors share the results of a small usability study.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.885
Threshold uncertainty score0.201

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.279
Teacher spread0.264 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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
Published2023
Admission routes3
Has abstractyes

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