Mobilising a Nation: RDM Training and Education in South Africa
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.
Bibliographic record
Abstract
The South African Network of Data and Information Curation Communities (NeDICC) was formed to promote the development and use of standards and best practices among South African data stewards and data librarians (NeDICC, 2015). The steering committee has members from various South African HEIs and research councils. As part of their service offerings NeDICC arranges seminars, workshops and conferences to promote awareness regarding digital curation. NeDICC has contributed to the increase in awareness, and growth of knowledge, on the subject of digital and data curation in South Africa (Kahn et al.,2014).NeDICC members are involved in the UP M.IT and Continued Professional Development training, and serve as external examiners for the UCT M.Phil in Digital Curation degree. NeDICC is responsible for the Research Data Management track at the annual e-Research conference in SA1and develops an annual training-focussed programme to provide workshop opportunities with both SA and foreign trainers. This paper specifically addresses the efforts by this community to mobilise and upskill South African librarians so that they would be willing and able to provide the necessary RDM services that would strengthen the national data effort.
 
 1eResearch conference: http://www.eresearch.ac.za/
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.059 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it