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Record W4232501856 · doi:10.11647/obp.0103.03

3. Technology Strategies for Open Educational Resource Dissemination

2016· book-chapter· en· W4232501856 on OpenAlexfundno aff
Phil Barker, Lorna M. Campbell

Bibliographic record

VenueOpen Education · 2016
Typebook-chapter
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsnot available
FundersDefense Language Institute Foreign Language CenterSchool of Liberal ArtsUniversity of British ColumbiaUniversity of Southern QueenslandJoint Information Systems CommitteeHigher Education AcademyUniversity of TasmaniaUniversity of EdinburghUniversity of Pittsburgh
KeywordsLearning analyticsAnalyticsKnowledge managementComputer scienceResource (disambiguation)Data science

Abstract

fetched live from OpenAlex

Global in perspective, this book argues strongly for the value of open education in both the developed and developing worlds. Through a mixture of theoretical and practical approaches, it demonstrates that open education promotes ideals of inclusion, diversity, and social justice to achieve the vision of education as a fundamental human right. A must-read for practitioners, policy-makers, scholars and students in the field of education.

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.998
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0040.010
Scholarly communication0.0230.020
Open science0.0020.007
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0300.008

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 head0.344
Teacher spread0.320 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations8
Published2016
Admission routes1
Has abstractyes

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