Bibliographic record
Abstract
The UK Library & Information Research Group (LIRG) research award is designed to promote research in library and information science. The aim of the award is to encourage and facilitate research by practitioners in the field. LIRG welcomes proposals from all sectors of the profession, and particularly invites practitioners to apply either as sole applicants, or in collaboration with academic or independent researchers. Applications from academics in library and information science departments are welcome and proposals that are submitted in partnership with practitioners are particularly welcome. The award is open to applicants from the UK only. The award is worth £1000, and designed to support small-scale research projects for which it may otherwise be difficult to find funding. The award may be used to defray research expenses (e.g. travel, postage costs), to fund attendance at high level meetings or to fund a study tour. However, a clear overall aim for the research must be stated. Guidelines for submission and evaluation criteria are on the LIRG web pages at: http://www.cilip.org.uk/specialinterestgroups/bysubject/research/activities/awards/researchaward.htm The closing date for submissions is February 29th, 2008 Applications and enquiries should be sent, preferably by e-mail, to: Dr Jean Yeoh LIRG Awards and Prizes Coordinator Information Services & Systems King's College London 4th Floor Waterloo Bridge Wing Franklin Wilkins Building 150 Stamford Street London SE1 9NN jean.yeoh@kcl.ac.uk 020 7848 4460
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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 | 0.018 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.421 | 0.320 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".