Bibliographic record
Abstract
To many geoscientists, especially those new to Canada, or new to academic positions within Canada, the NSERC ‘system’ may appear somewhat reminiscent of the impenetrable black obelisk in the classic film “2001, A Space Odyssey”. The general function of the obelisk, and the language resounding from out of it, can, at times, seem obscure. Applicants are, for example, sometimes baffled (as has been the experience of this author) – when they receive unanimously positive comments from external reviewers with a notice that funding was reduced or not even granted at all. How can this be? As a member of the Solid Earth Grant Selection Committee (GSC08) over the past three years, and chair of the committee in the last year, I will make an effort here to expose several growing issues for geoscientists across Canada, who access NSERC for their research funds. This commentary highlights problems evident to me as a reader of NSERC proposals, and attempts to disseminate information to the greater community, to remove some of the mystery behind decisions that come out of NSERC competitions. Furthermore, I wish to make the point that the NSERC system is not sustainable in light of changing demographics in the scientific community, ongoing budgetary constraints and new funding for Canadian science, such as the Canada Foundation for Innovation (CFI) and Canada Research Chair (CRC) programs. The centerpiece of the commentary is the Discovery Grants program (previously known as Research or Operating Grants), which I view as the “meat and potatoes” and most cherished money of many researchers. NSERC DG grants seem the most difficult to increment, but they are the backbone of our curiosity driven research efforts. Other perhaps less publicized aspects of this and other programs are also mentioned.
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 | 0.082 | 0.249 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.027 | 0.041 |
| Scholarly communication | 0.031 | 0.027 |
| Open science | 0.014 | 0.009 |
| Research integrity | 0.055 | 0.067 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".