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Record W4396513767 · doi:10.4324/9781003282075-9

DigitalNWT—adapting digital tools to support remotely managed digital literacy research, education, and communications in Northern Canada

2024· book-chapter· en· W4396513767 on OpenAlexaboutno aff
Rob McMahon, Michael B McNally, Samantha Blais, Murat Akçayır, Kyle Napier, Leanne Goose, Kevin Zhu

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsnot available
Fundersnot available
KeywordsDigital literacyLiteracyMultimediaTelecommunicationsComputer scienceWorld Wide WebSociologyPedagogy

Abstract

fetched live from OpenAlex

Digital information and communication technologies offer researchers, educators, and communicators opportunities to connect and collaborate with people located in geographically rural / remote and Indigenous communities. The COVID-19 pandemic illustrated the importance of adapting digital tools to support productive and reciprocal relationships with diverse communities over long distances. Many projects face significant structural, ethical, and methodological barriers to the critical adaptation of digital information and communication technologies. This chapter examines ways to support community-led digital inclusion and digital literacy in rural Indigenous communities in the Northwest Territories, Canada, through participatory action research. Drawing on researcher reflections, and interviews with eight local researchers and digital innovators, this chapter discusses how a team of southern university-based and Northern-based partners worked together to adapt digital tools and participatory processes to address the limitations imposed by COVID-19.

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 categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.820
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0000.001
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 head0.072
GPT teacher head0.304
Teacher spread0.233 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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