Who's in library / Nurul Syahirah Md Salleh, Nur Syazwani Bakar and Norli Ezzati Mohd Abu Hassan AlShari
Bibliographic record
Abstract
Discuss about the leadership in the library which is dealing with information management services. Karin Witternborgh, Mastura Hj. Muhamad and Ingrid C. Langhammer are well-known person of leadership in the field of Information Management. Karin Witternborgh was a well-known leader in the academic librarya, the University Library of Virginia. She was the recipient of the Elizabeth Zinft Leadership Award in the year of 2004. While, Mastura Hj. Muhamad is the person in the public library, Perbadanan Perpustakaan Awam Selangor. She was contributed rebranding process in Raja Tun Uda Library and other regional libraries. She has organized various programs to attract people to come to the library and create knowledge-based society, cultured reading and supporting life-long learning. Ingrid C. Langhammer was the well-known person of Special Libraries and Information Services. She was the recipient of the Canadian Association of Special Libraries and Information Services (CASLIS) Award for special Librarianship in Canada in 2011.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.098 | 0.028 |
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".