The impact of the global movement of open access (OA) on OA publishing for Canadian government science researchers
Bibliographic record
Abstract
Open access (OA) has been an enabling and accelerating factor for a more open scientific discovery process. In March 2011, the Government of Canada announced its commitment to Open Government along three streams: open information, open data, and open dialogue. The tenth anniversary statement of the Budapest Open Access Initiative has also encouraged many academic institutions to adopt and implement OA policies and best practices. This case study explores the global OA movement’s impact on researchers from federal science departments and agencies. The authors will assess whether mandates in OA and Open Science (OS) initiatives have led to a growth of Open Government resources and how the availability of open publications contributes to research impact assessment and impacts researchers at all stages of their careers. The authors use the Web of Science (WoS) Core Collection to locate OA publications by federal government scientists and citation trends among the OA types. WoS and InCites Benchmarking & Analytics are used to compare the growth of OA publications against key dates identified by the OA movement and Canada’s OS initiatives. Science librarians and information professionals are increasingly well versed in how OA opens up new areas of activity and accelerates innovation across disciplines. However, our knowledge of OA publishers of high impact in science disciplines, and experiences with federal S&T activities reporting and publishing through OA are limited. We hope to develop an enhanced understanding of the current OA landscape and the requirements for a government research system that supports OA and OS.
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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 | 0.021 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.023 |
| Science and technology studies | 0.035 | 0.016 |
| Scholarly communication | 0.034 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".