and Oceania Region � 14–17 ALP News � 18–21 Around the Region � 22–25 Events � 26–27 Events/Book Review � 28
Bibliographic record
Abstract
am writing this short review in the midst of planning our open session for Quebec. Since the Durban conference, we have been quite busy following up on recommendations from our Standing Committee (SC) meeting. We had a very productive mid-term meeting in February at the Indira Gandhi National Open University, New Delhi. Mid-term meetings are extremely important for the Section for several reasons. It allows members from all parts of the Asia Oceania region to meet uninterrupted over a period of days to plan strategies and programmes for the diverse region. It is an avenue for promoting IFLA in the host country. Further, it provides networking opportunities amongst the Committee and also with librarians from the host country. We met some of our members for the first time at the Delhi meeting. Every one of our members, including several new members actively participated in the meeting and made valuable contributions to our discussions. We noticed that there has been an increased awareness of the role and work of IFLA as well as the Regional Standing Committee Asia Oceania. In addition to our two-day meeting, SC members participated in two television programmes where we had an excellent opportunity to showcase IFLA and especially the Section throughout the Indian sub-continent. (A detailed account can be found on page 13). Again, we would like to record our sincere gratitude to our hosts, especially to Prof V. N. Rajasekharan Pillai, the Vice-Chancellor and Prof Uma Kanjilal and Prof Jaideep Sharma for their thoughtful
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.137 | 0.074 |
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".