Knowledge for All: Building a Collaborative, International, and Open Citation Database.
Bibliographic record
Abstract
Collaborative internet technologies and thriving open access, open source, and open data movements have fostered many projects that provide free access to scholarly journal citation data. This is vitally important for ensuring that researchers, policy makers, libraries, non-profit organizations, and the general public globally have access to all pertinent research, regardless of institutional affiliation or financial resources. However, there does not currently exist a tool that instead provides comprehensive access to all published scholarly journal citation data in a completely open format. Knowledge for All is that project. Using a collaborative, crowdsourcing model in all respects, Knowledge for All is an open access and open source scholarly citation database that is being developed by a non-profit organization in Atlantic Canada with the support of thousands of organizations and individuals worldwide. All data in the Knowledge for All system will be available in the public domain for re-use in any capacity, as well as being publicly available through a web interface with robust search features. As a project that will be collaboratively developed, maintained, and used by the international research community, it is vitally important that the project meets the community’s diverse needs. Thus, the PKP Conference provides an opportunity to present the working technology, content, funding, and governance models for the Knowledge for All project to the community to gather feedback and generate discussion and new ideas, particularly regarding how can we engage the entire international community in the Knowledge for All project.
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 | 0.036 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.038 | 0.053 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.024 | 0.028 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.023 |
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".