Flexible Networking in Research Capacity Building at the National University of Laos: Lessons for North-South Collaboration
Bibliographic record
Abstract
ABSTRACT –This paper describes a research-training project for building social science research capacity at the National University of Laos (nuol), supported by idrc. At the international level, the project was structured as a flexible network of resource persons from six countries. The main successes of the project turned out to be unantici-pated: the project offered significant insights to nuol’s ongoing assessment of its administrative capacity to manage university-based research. The flexibility of the network approach, combined with a structure that oriented the inter-national network toward engaging with, and responding to, needs articulated by nuol staff, proved crucial to the project’s ability to respond to changing institutional needs in nuol. The importance of paying careful attention to how projects can be structured so that they are responsive to Southern needs has been heightened by intensified pres-sure in Canadian universities to generate funds, while promoting excellence in research and training takes a backseat. RÉSUMÉ – Cet article décrit un projet de formation pour améliorer la capacité de recherche en sciences sociales à l’Université nationale du Laos (undl). Le projet était subventionné par le crdi. Il a été structuré, au niveau international, sous la forme d’un réseau flexible de personnes-ressources de six pays. Les résultats les plus probants du projet furent tout à fait inattendus. En effet, pour l’undl, il fit la lumière sur l’autoévaluation, en cours, de sa capacité administrative à gérer la recherche universitaire. La flexibilité de l’approche en réseau, jumelée à une structure qui orientait le réseau international vers l’engagement et la réponse aux besoins exprimés par le personnel même de
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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 | 0.041 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.022 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".