The impact of the global movement of open access (OA) on OA publishing for Canadian government science researchers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.013 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it