FACTORS INFLUENCING FUNDED RESEARCHER PRODUCTIVITY OF EDUCATION FACULTIES: AN EMPIRICAL INVESTIGATION OF THE PUBLICATION PERFORMANCES WITHIN CANADIAN UNIVERSITIES, 2001-2008
Bibliographic record
Abstract
Our study seeks to identify the factors that explain the research productivity of education faculties, within seven different universities in Quebec-Canada. The main hypothesis of this study is that productivity in scientific research is significantly influenced by the volume and origin of the funding sources mobilized to support scientific research performance. Based on a sample of 194 researchers and time series data (2001-2008), our research use individual publications in referred journals (number of publications, fractioned publications, citations, impacts) as surrogates for research productivity. Not surprisingly, the findings show that funding is a key input in the scientific production process, and, in turn, in education researcher performance, taken individually. Examining the specific effects of funding sources on productivity, we found that, among the sources for which data were available, only funding from the federal government and the private sector are not statistically significant in its relation to the productivity indicators used. Also not surprisingly, findings show that academic funding from grants and university research funding councils provide the greatest elasticity regarding outputs dealing with the number of publications. Finally, we find that age, gender, size and language (Francophone versus Anglophone) of university instruction, funding councils, grants and provincial government funding significantly affect researcher productivity. Our results raise questions about whether financial incentives boost publication productivity, and whether policy-makers should place greater emphasis on other relevant factors of high productivity among researchers, faculties and departments operating in the education field.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.022 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".