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Record W4401852468 · doi:10.1007/s10758-024-09776-9

Aligning Digital Educational Policies with the New Realities of Schooling

2024· article· en· W4401852468 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueTechnology Knowledge and Learning · 2024
Typearticle
Languageen
FieldComputer Science
TopicDigital literacy in education
Canadian institutionsUniversité Laval
FundersDublin City University
KeywordsContext (archaeology)Agile software developmentEconomic growthPolitical scienceMacroCoronavirus disease 2019 (COVID-19)Sociology of EducationSociologyPublic relationsSocial scienceEconomicsGeographyManagement

Abstract

fetched live from OpenAlex

Abstract To make sense of the changes provoked by the Covid-19 pandemic and its immediate aftermath, this paper critically examines digital education policy responses in the context of the ‘new realities’ faced by schooling. Based on seven case studies contributed by authors from Australia, India, Ireland, Italy, Japan, Canada, Sri Lanka, two key questions are addressed: (1) What are the ‘new realities’ of schooling post Covid-19? and (2) How have digital educational policies changed in response to the new realities of schooling? Findings highlight the complexity of the problem of aligning digital education policies at the macro level to the realities experienced at the meso and micro levels of schooling systems. The paper concludes with discussion of the need for, and challenges of, agile policy making at all levels (macro, meso and micro) that are necessary for schooling systems to meet the challenges and realities of a complex changing world.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.264
Teacher spread0.258 · 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