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Record W1931366832 · doi:10.47678/cjhe.v39i3.478

International Development and Research Capacities: Increasing Access to African Scholarly Publishing

2010· article· en· W1931366832 on OpenAlexaffvenueabout
Amy Scott Metcalfe, Samuel Smith Esseh, John Willinsky

Bibliographic record

VenueCanadian Journal of Higher Education · 2010
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPublishingScholarly communicationPromotion (chess)Political scienceElectronic publishingSociologyLibrary sciencePublic relationsThe InternetWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

This paper examines the evolving relationship between Canada and the African academic research community through the promotion of a concept known as Information and Communication Technology for Development (ICT4D) and with an eye to its implications for increasing the circulation of research through such means as open access (OA) publishing models. We analyze the programmatic discourse of Canada’s International Development Research Centre’s (IDRC) African research initiatives, and report on an IDRC research and development project assessing the means of increasing access to African scholarly journals through the use of open source software platforms and open access publishing and archiving models. Consistent with IDRC’s multi-year effort to contribute directly to university-based research capacities by investing in ICT infrastructure in Africa, our survey of African editors, librarians, and faculty from five African nations reveals a similar interest in developing those capacities, despite numerous challenges, through the use of online publishing systems and OA publishing models, which hold some promise of increasing access to research published in Africa.

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 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)0.032
metaresearch head score (Gemma)0.062
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Bibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0320.062
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0680.039
Science and technology studies0.0000.000
Scholarly communication0.0430.009
Open science0.0030.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.603
GPT teacher head0.582
Teacher spread0.021 · 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; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations9
Published2010
Admission routes3
Has abstractyes

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