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Record W2885925209 · doi:10.1007/s10758-018-9384-x

Digital Agency: Empowering Equity in and through Education

2018· article· en· W2885925209 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTechnology Knowledge and Learning · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicE-Government and Public Services
Canadian institutionsDurham College
FundersJapan Society for the Promotion of Science
KeywordsCompetence (human resources)Digital societyAccountabilityEquity (law)Agency (philosophy)Public relationsPolitical scienceEngineering ethicsBest practiceKnowledge managementSociologyEngineeringComputer scienceEconomicsManagementSocial science

Abstract

fetched live from OpenAlex

This theoretical paper is concerned with conceptualising a major issue that faces all those concerned with and charged with influencing the future of equity in education—the need for digital agency (DA). The paper offers a rationale for this concern, highlights the importance of the concept and its practices, presents the challenges it brings, some current ways in which practices are tackling these challenges, and considers the theoretical foundation for how it might be addressed further in the future. The paper defines DA, and its three component parts—digital competence, digital confidence, and digital accountability. The paper argues that DA is a fundamental requirement for and through education, that it affects all citizens in a global society, and should be enabled through their ongoing and developing digital practices. The paper concludes with recommendations for different educational groups—including policy makers, practitioners, developers, and researchers.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.867
Threshold uncertainty score0.301

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 science0.0000.000
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 head0.024
GPT teacher head0.377
Teacher spread0.353 · 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