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Challenges and Opportunities for Open, Distance, and Digital Education in the Global South

2022· book-chapter· en· W4220749663 on OpenAlex
Tony Mays

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

VenueHandbook of Open, Distance and Digital Education · 2022
Typebook-chapter
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
FundersBrigham Young University
KeywordsDistance educationCommonwealthOpen educationRelevance (law)Political scienceArgument (complex analysis)Open learningGlobal SouthEngineering ethicsPublic relationsEngineeringKnowledge managementSociologyPedagogyGeographyComputer scienceTeaching methodMedicine

Abstract

fetched live from OpenAlex

Abstract This chapter explores some of the challenges and opportunities for expansion of open, distance, and digital education in the global south. The discussion begins by defining the terms as used in the chapter and explains why such approaches are of relevance to the diverse countries involved. The chapter then provides some current examples of open, distance, and digital education provision and how some of these practices have been adapted in response to external factors such as climate, financial, and pandemic crises. The chapter then discusses the challenges and opportunities indicated both by current practice and by current research into issues such as open pedagogy, technology-enabled learning, and educational financing. The chapter then makes an argument for the development of more resilient, future-directed education provision, drawing heavily on the experience of the Commonwealth of Learning in its efforts to support sustainable development through learning.

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.820
Threshold uncertainty score0.999

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 communication0.0020.002
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 head0.097
GPT teacher head0.365
Teacher spread0.268 · 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