Bibliographic record
Abstract
Scientists are among the most enthusiastic people when it comes to talking about their work. Despite this, it seems that it is the funding system that Canadian scientists most often discuss these days, and not their findings and new ideas. Needless to say, research can only exist with good and secure funding, but when obtaining funding becomes a dominant part of investigators’ activity, the system has a problem. I am a cell biologist and physiologist recruited to Canada from Hungary. Generous funding helped me to complete my post-doctoral training here and to start my own lab a decade ago. The transition from post-doc was smoothened by one of the last Canadian Institutes of Health Research (CIHR) Senior Research Fellowships that gave 2 years of post-doctoral funding and 2 years of new investigator salary support. I was extremely lucky to have this opportunity, which is no longer available to current post-docs. However, as my independent work started to produce results, I had to realize how difficult it was to maintain even a modestly sized lab (one technician and two students) within a research institute. As the reality of inevitable rejections of first grant renewal applications set in, I was swept away by the struggle for funding and the never-ending cycles of reviewing. This experience, shared by many of us, shaped my views on the ills of the Canadian biomedical research funding system. I am sharing my thoughts on this important and complex topic, with the hope that it will be part of a fruitful discussion.
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 | 0.170 | 0.249 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.027 | 0.036 |
| Scholarly communication | 0.035 | 0.031 |
| Open science | 0.009 | 0.021 |
| Research integrity | 0.044 | 0.090 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".