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Record W7000104886

Engagement in the Knowledge Economy: Regional Patterns of Content Creation with a Focus on Sub-Saharan Africa

2017· article· en· W7000104886 on OpenAlexfundno aff

Bibliographic record

VenueLeicester Research Archive (University of Leicester) · 2017
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsnot available
FundersSimon Fraser UniversityEuropean Commission
KeywordsDemocratizationEnablingContent creationKnowledge economyKnowledge creationThe InternetDomain knowledgeDigital contentPublic domain
DOInot available

Abstract

fetched live from OpenAlex

Increasing digital connectivity has sparked many hopes for the democratization of information and knowledge production in sub-Saharan Africa. To investigate the patterns of knowledge creation in the region compared to other world regions, we examine three key metrics: spatial distributions of academic articles (traditional knowledge production), collaborative software development, and Internet domain registrations (digitally mediated knowledge production). We find that, contrary to the expectation that digital content is more evenly geographically distributed than academic articles, the global and regional patterns of collaborative coding and domain registrations are more uneven than those of academic articles. Despite hopes for democratization afforded by the information revolution, sub-Saharan Africa produces a smaller share of digital content than academic articles. Our results suggest the factors often framed as catalysts in the transformation into a knowledge economy do not relate to the three metrics uniformly. While connectivity is an important enabler of digital content creation, it seems to be only a necessary, not a sufficient, condition; wealth, innovation capacity, and public spending on education are also important factors.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score0.791

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.236
GPT teacher head0.379
Teacher spread0.144 · 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 teacher head, not a consensus.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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