Bibliographic record
Abstract
Purpose This paper seeks to describe a pilot project for the Federal Science eLibrary to measure the impacts on Government of Canada researchers when provided with seamless, equitable access to an expanded core of electronic journals in science, technology and medicine (STM). The Federal Science eLibrary is an initiative supported by the Strategic Alliance of Federal Science and Technology Libraries to provide improved access to information at the desktop for the 22,000 Canadian federal scientists, policy analysts and decision makers. The pilot project was designed to evaluate the benefits of increased access to e‐journals at the pilot sites and test network performance in connecting to a central digital repository. Design/methodology/approach A total of 500 users in three Canadian government sites with limited access to electronic resources were provided with full text access to a digital repository of over 3,000 e‐journals over a 12‐week period. Questionnaires, teleconferences, usage statistics and e‐mail correspondence were used to gather and measure researchers' response and show impacts on their ability to do their work. Findings Pilot groups reported significantly reduced time finding and verifying information. Time saved was redirected into critical activities such as research, laboratory activities, manuscript preparation, peer review activities and professional reading. Participants found that increased desktop access had a very positive impact on their ability to do their work. Originality/value This study shows the benefits of expanded access to electronic journals for federal government scientists through a Federal Science eLibrary initiative.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".