Scientists' collaboration in the social sciences field: Investigating the determinants of scholarly collaboration in the Canadian context 2001–2008
Bibliographic record
Abstract
In the era of knowledge-based economies, knowledge production and transfer have emerged as a crucial component of innovation and human capital development. Science activities are globalizing and research partnerships will become increasingly imperative. Hence a considerable trend in research collaboration has been noted in the literature. Over the last few years, collaboration among scientists has been on the rise [1] and the different ways in which this collaboration takes place have been the subject of many conceptual [2] and empirical studies [3]. Furthermore, the analysis of the relationship between research inputs (grants, infrastructure spending, training of researchers, etc.) and research outputs (collaboration, productivity, citation, impact, etc.) has also been the subject of several explanatory studies, mostly done in OECD countries, whether in France [4], the United States [5], Italy [6], New Zealand [7], the United Kingdom [8], Australia [14], or the European Union [9].
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.027 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".