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Record W7108464561 · doi:10.14749/30773750

South African National Survey of Research and Experimental Development sector results and trends: higher education sector R&D at a glance 2020/21. Fact sheet 45

2025· article· en· W7108464561 on OpenAlexaboutno aff

Bibliographic record

VenueHuman Science Research Council SA · 2025
Typearticle
Languageen
FieldDecision Sciences
TopicProfessional Masters Programs Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsHigher educationWorkforceFact sheetQuarter (Canadian coin)Further educationWorkforce development

Abstract

fetched live from OpenAlex

South Africa's higher education sector plays a pivotal role in the national system of innovation it houses the largest R&D workforce in the country, with two-thirds of total R&D personnel working in this sector. In 2020/21, higher education was responsible for 41.1% of total R&D spending in the country. Natural sciences, technology and engineering continue to dominate higher education research, but only marginally more so than the social sciences and humanities. Researchers from other countries make up almost a quarter of the total in higher education, most of them postgraduates.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.996
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.016
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0100.003

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 head0.580
GPT teacher head0.536
Teacher spread0.045 · 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 designObservational
DomainEvaluation
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
Published2025
Admission routes1
Has abstractyes

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Same venueHuman Science Research Council SASame topicProfessional Masters Programs AnalysisFrench-language works237,207