VIRTUAL INTERNATIONAL CONFERENCE ON IS OPEN ACCESS KNOWLEDGE CRITICAL IN SCHOLARLY COMMUNICATION?
Bibliographic record
Abstract
Office of Research, Innovations & Commercialization, ILMA University as always plays a significant role of stimuli to provoke the understanding of publishing protocols among the publishers and other stakeholders of scholarly communications. In continuation to this role, Office of Research, Innovations & Commercialization-ILMA University is hosting a virtual international conference on IS OPEN ACCESS KNOWLEDGE CRITICAL IN SCHOLARLY COMMUNICATION? With this note, to spread growing significance of Open Access Knowledge in Scholarly Communication, I am extending an Official Invitation to your good self to attend this conference. During this extraordinary new normal time in an unprecedented year, there is no pressure to attend this conference. The conference has been designed to be as flexible as possible in the hopes that many people can participate to listen Conference KEYNOTE SPEAKERS from Higher Education Commission, Govt. of Pakistan, Web of Science, Elsevier, COPE, Creative Commons, SAGE Open, University of Jyväskylä, Finland, University De Quebec Montreal, Commonwealth University and Suan Sunandha Rajabhat University, Bangkok.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.022 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.175 | 0.059 |
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".