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Record W2891221095

Digital agency to empower equity in education : Summary Report

2017· article· en· W2891221095 on OpenAlexfundno aff
Miri Shonfeld, Don Passey, Lon Appleby, Miriam Judge, Toshinori Saito, Anneke Smits, Louise Starkey

Bibliographic record

VenueLancaster EPrints (Lancaster University) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Leadership and Innovation
Canadian institutionsnot available
FundersUniversitetet i OsloUniversity of MumbaiVictoria University of WellingtonTel Aviv UniversityVictoria UniversityCurtin University of TechnologyTata TrustsGriffith UniversityUniversity of North TexasUniversity of WollongongAl Akhawayn University in IfraneItä-Suomen YliopistoUniversité de SherbrookeVrije Universiteit BrusselKing's College LondonDublin City UniversityUniversiteit van AmsterdamMonash UniversityUniversité LavalWest Virginia UniversityUniversity of CanterburyTata Institute of Social SciencesManchester Metropolitan UniversityUniversity of OtagoArizona State UniversityNova Southeastern UniversityKasetsart University
KeywordsAgency (philosophy)Equity (law)Public relationsEmpowermentPolitical scienceEconomic growthBusinessSociologyEconomicsSocial science
DOInot available

Abstract

fetched live from OpenAlex

In EDUsummIT 2017, Thematic Working Group (TWG) 4 researched digital agency empowering equity in education. In a world where digital engagement with learning is increasing, both onsite and online, it is important that concepts and concerns of digital agency are considered appropriately by policymakers and practitioners when they develop and implement provision for learners, locally, regionally, nationally and internationally.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.010
Science and technology studies0.0010.001
Scholarly communication0.0070.007
Open science0.0010.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0180.005

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 head0.376
Teacher spread0.301 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations2
Published2017
Admission routes1
Has abstractyes

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