Managing regional centres' of expertise collaborations with stakeholders including higher education institutions
Bibliographic record
Abstract
Purpose – The purpose of this paper is to assist the United Nations Regional Centres of Expertise (RCEs) in continuing their fundamental work within the region and to address some of the prominent challenges within the RCE community. Specific RCE case studies from the global network were employed, emphasizing experiences in collaboration with multiple stakeholders including higher education institutions. Design/methodology/approach – Conducting a literature review and employing a qualitative research methodology with the use of a guided questionnaire, the paper aims to gain a deeper understanding of the operations of RCEs in general and more specifically the case studies. Findings – The paper shows some of the strategies implemented by the cohort of case studies to overcome their common challenges. Key recommendations based on the findings are made in its quest for continual development and final conclusions assessing the contentious challenges are drawn. Research limitations/implications – This paper focuses on RCEs within Europe, with cases from the USA and Canada for comparison. Although the paper highlights common themes and challenges, it is highly probable that RCEs outside of the studied regions may contend with similar challenges; further research would have to be conducted to assess the wider scope of the situation. Originality/value – The paper gives an external perspective of the challenges faced and identifies some areas in which improvements could be made. It is also generated from information gathered from multi-case study RCEs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".