The Centre for Evidence Based Library and Information Practice (C-EBLIP) Director’s Report to the Dean on 2016/17
Bibliographic record
Abstract
2016/17 saw a continuation of the work begun the previous year. The C-EBLIP Research Network (CRN) was formed. The CRN was an affiliation of international institutions interested in librarians as researchers and/or evidence based practice. Great strides were made in recruiting member institutions from around the world. As of April 2017, members included the following affiliates: University of Windsor Library, Windsor, ON, Canada Concordia Library, Montreal, PQ, Canada University of Victoria Library, Victoria, BC, Canada University of Alberta Libraries, Edmonton, AB, Canada Carleton Library, Ottawa, ON, Canada University of Northampton Library, Northampton, UK LARK Library Applied Research Kollektive (ALIA group), Australia University of Toronto Libraries, Toronto, ON, Canada Mount Royal University Library, Calgary, ON, Canada Cambridge University Library and Office of Scholarly Communication, Cambridge, UK Western Libraries, London, ON, Canada UBC Okanagan Library, Kelowna, BC, Canada Flinders University Library, Adelaide, Australia University of Regina Library, Regina, SK, Canada University Library, University of Saskatchewan Hong Kong Baptist University Library University of Southern Queensland Library, Australia Public Health England Knowledge & Library Services, Bristol, UK Loyola Marymount University Library, Los Angeles, CA, USA Health Research Board, Dublin, Ireland This work aligned with the internationalization efforts of the University of Saskatchewan, and further enhanced the University Library’s reputation as an institution strong in librarian researchers. I travelled to Toronto in February of 2017 to give a poster presentation at the Ontario Library Association’s Super Conference about the Research Network. Momentum was really picking up after a year of recruitment and promotion. Unfortunately, due to the poor provincial budget and concerns from new library leadership about return on investment and sustainability, the C-EBLIP Research Network was dismantled in April 2017. While C-EBLIP is still focused on supporting librarians as researchers and promoting evidence based library and information practice, the opportunity for expansion and further developments in this area outside of the University Library has been curtailed.
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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 | 0.149 | 0.215 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.028 | 0.011 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.021 | 0.020 |
| Insufficient payload (model declined to judge) | 0.111 | 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".