Bibliographic record
Abstract
The phrase information commons refers to our shared knowledge base. Heather Morrison presents examples of the commons in action, ranging from open access and open source scholarly resources to the blogosphere. The concept of sampling in music is discussed, and applied to librarianship. Key policy for the commons are identified and discussed, including open access, telecommunications issues (net neutrality, access issues), and copyright laws that facilitate sharing. Olivier Charbonneau presents "tools for the shepherd", or when digital projects are fit for collaboration, in the context of Lessig's regulatory framework, Benkler's "commons based peer production" framework, and Alter's "Work Centered Analysis Framework for Systems Analysis". The Canadian Legal Information Institute (CANLII), created to enable free access to authoritative versions of Canadian case law and statutes on the Internet through a uniform search interface, is presented as an example of a collaboratively produced digital commons.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.024 | 0.021 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.056 | 0.013 |
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".