Factors Influencing Research Performance in Higher Education: An Empirical Investigation
Bibliographic record
Abstract
Universities play an increasingly significant role in producing new knowledge. The relationship between research inputs (grants, infrastructure spending, training of researchers) and research outputs (number of publications, citation, impact) emerges, therefore, as a strategic issue for public decision-making on funding in support of innovation and the development of competencies. Despite the abundance of empirical works on the question of researcher productivity, there is a paucity of studies dealing with this issue in the context of higher eductaion. This paper seeks to identify the factors that explain research productivity in higher education, using as a case study, the universities in Quebec-Canada. The main hypothesis is that productivity in scientific research is significantly influenced by the volume and origin of the funding sources mobilized to support scientific research performance. We analyzed data on 194 researchers for the period of 2001–2008. Individual publications in referred journals (number of publications, fractioned publications, citations, impacts) were used as indicators for research productivity. Factor analysis and linear regression served as tools for evaluation. Our findings imply that the volume of funding is not as influential as supposed. We revealed that age and language (Francophone versus Anglophone) of university instruction, and, in addition, the origin of funding do affect researcher productivity. Generally speaking, young researchers, as well as those affiliated with Anglophone or/and large universities tend to produce more publications. The gender of researcher does not seem to significantly influence the productivity variables. The results of our analysis should motivate program evaluators who assess the benefits of public funding andintervention to support academic research. It is essential thatevaluators do not only see these benefits in terms of number of publications produced, but also through the prism of publication quality (citations and outcomes generated) as well as individual and organizational attributes. In this way, those designing interventions to support research will benefit from the fully-fledged information necessary to improve program effectiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".