Connecting the Knowledge Commons: From Projects to Sustainable Infrastructure
Bibliographic record
Abstract
ELPUB 2018 marks the 22nd edition of the International Conference in ELectronic PUBlishing and the 10th anniversary of the meeting being held in Toronto. ELPUB has featured research results in various aspects of digital publishing for over two decades, involving a diverse international community of librarians, developers, publishers, entrepreneurs, administrators and researchers across the disciplines in the sciences and the humanities. It is unique as a platform for both researchers, professionals and the broader community. The Conference is held annually and contains a multi-track presentation of refereed papers as well as invited keynotes, special sessions, demonstrations, and poster presentations. The entire collection of conference papers since its inception is available in the ELPUB Digital Library. \n\n\n\n The theme of ELPUB 2018 is Connecting the Knowledge Commons: From Projects to Sustainable Infrastructure. The question of sustainability in the open access movement has been widely debated, yet satisfactory answers have yet to be generated. \n\n\n\n How do we move from an approach entirely based on temporary projects to an approach based on community-based sustainable infrastructure? \n\n What kinds of social and technical infrastructures could support the Knowledge Commons? \n\n What values and services are being delivered, by which stakeholders, and for whom? \n\n What governance and financial models are possible? \n\n Given the global nature of scholarly communication, how do we ensure that the designs of the Commons are inclusive of voices from the global South?
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.024 | 0.026 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.009 |
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".