Bibliographic record
Abstract
Canada is not yet maximising the return on its public investment in research. Canadian research councils spend 1.5 billion dollars annually. Canada produces at least 50,000 research journal articles per year, but if it is worth funding and doing at all, research must be not only published, but used, applied and built upon by other researchers (‘citation impact’). The online-age practice of self-archiving has been shown to increase citation impact by a dramatic 50-250%, but so far only 15% of researchers are doing it spontaneously. Citation impact is rewarded by universities (through promotions and salary increases) and by research-funders such as SSHRC (through grant funding and renewal) at a conservative estimate of 100 dollars per citation. If we multiply this by the 85% of Canada's annual journal article output that is not yet self-archived, this translates into an annual loss of 2.125 million dollars in revenue to Canadian researchers for not having done (or delegated) the few extra keystrokes per article it would have taken to self-archive their final drafts. But this impact loss translates into a far bigger one for the Canadian public, if we reckon it as the loss of potential returns on its research investment. As a proportion of the Canada’s yearly 1.5 bn dollars research expenditure (yielding 50,000 articles x 5.9 = 295,000 citations), our conservative estimate would be 50% x 85% x 1.5 bn = 640 mn dollars worth of loss in potential research impact(125,375 potential citations lost). The solution is obvious, and it is the one the RCUK is proposing in the UK: to extend the existing universal 'publish or perish' requirement to 'publish and also self-archive your final draft on your institutional website'. The time to close this 50%-250% research impact gap is already well overdue. This is the historic moment for Canada to set an example for the world, showing how to maximise the return on the public investment in research in the online era.
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 | 0.118 | 0.275 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.013 | 0.030 |
| Scholarly communication | 0.043 | 0.057 |
| Open science | 0.012 | 0.015 |
| Research integrity | 0.018 | 0.026 |
| Insufficient payload (model declined to judge) | 0.027 | 0.019 |
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".