Bibliographic record
Abstract
The Commons, a term derived from the concept of common grazing ground in simpler times, is now used to describe our shared knowledge-based, and the processes that facilitate or hinder its use. This session focuses on recent activities by Canadian librarians towards creating the commons, through open access and open source approaches. The Canadian Association of Research Libraries, the Canadian Library Association (CLA), and the British Columbia Library Association have policies strongly in support of open access. E-LIS, the open archive for library and information studies, provides a means for librarians to share work through self-archiving, and is an interesting example of a new type of global collaboration. CLA's Evidence Based Librarianship Interest Group has developed a new, international, peer-reviewed open access journal, and The Partnership (of library associations across Canada) has another in the works. A new concept of open source scholarship (open sharing of content, rather than software) is explored, with examples such as the Human Genome Project and Useful Chemistry. Canadian librarian scholarly blogging and wikis are discussed. It is concluded that the commons offers new opportunities for sharing and global collaborations, the like of which we have never seen before. It will be important to develop copyright laws that facilitate sharing, not just intellectual property protection.
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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.045 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".