The Effect of Gender and Funding on Research Performance
Bibliographic record
Abstract
In spite of various improvements and increasing involvement of female researchers in scientific \nactivities in recent years, the gender gap still persists and women remain greatly \nunderrepresented in technology, engineering, and computer science fields. This thesis attempts \nto shed some light on the effect of gender and funding on research output of Canadian \nresearchers in natural sciences and engineering. In this research, using NSERC and Scopus \ndata from 1982 to 2018, we apply descriptive statistical analysis and regression analysis to \nstudy the influence of funding and gender on the quantity of published journal papers and their \nscientific impact. The study concludes that funding has a positive impact on both the number \nof papers published and the number of citations received by their respective author. However, \nwe also observe that as career age of authors increases, researchers become less productive, \nthey publish less papers and their citation counts slightly diminish with time as well, even \nthough their funding amounts typically increase. In terms of gender, even though we find that \nfemale researchers are indeed greatly underrepresented and receive lower amounts of funding \nthan their male counterparts, they produce on average similar number of articles with similar \nscientific impact. This means that female researchers can generate comparable research output \nwith lower research costs compared to male researchers and are thus more efficient in their \nresearch production. These findings suggest that governmental funding agencies should \nintroduce more effective gender-related funding strategies and greater support for early-career \nresearchers.
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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 | 0.018 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".