Bibliographic record
Abstract
Stepping into someone's shoes.That is the metaphor that came to mind as I transitioned into the Editor-in-Chief role with this issue of JCHLA.After five years of exemplary work on this journal, Sophie Regalado stepped down, and I became Editor-in-Chief.But the more I thought about it, the less the metaphor seemed to make sense.Was I actually stepping into Sophie's shoes?Her exact shoes?The shoes that she selected, tried on, purchased, wore in, and was comfortable in?Those shoes?Actually, I was not.I believe the process was more like selecting my own new shoes.But selecting them with the guidance of someone who knew shoes Á knew the brands, knew the advantages and disadvantages of each, but who let me make the decision.I chose my own shoes.Sophie observed as I made my choice and then made sure that they were a good fit, that they suited me well, and that I would be comfortable in them in the long term.I've come to realize that those are the qualities of a strong and effective mentor.A good mentor walks side by side the mentee, sharing knowledge and experience until the mentee can not only walk on her own but can also start the process again with another mentee.Thank you Sophie for your patience, your guidance, your expertise, and your honesty.To celebrate Sophie's work on JCHLA, our new ''In Focus'' column features Senior Editor Heather Ganshorn's interview with Sophie.This issue features two peer-reviewed articles.The first is a program description by Maria Buda of the University of Toronto Dentistry Library.Her article entitled ''Collection inventory in a Canadian academic dentistry library'' will be of interest to all libraries considering the task of undertaking a major collection inventory.Like many CHLA chapters, the Health Library Association of British Columbia's members face the challenge of distance for meetings and continuing education events.In her article ''The HLABC Webcasting/Webconferencing Pilot Project'', Devon Greyson shares the experience of trying to meet that challenge using online technology.Trish Chatterley, our new Junior Editor, has compiled Chapter Highlights to keep us abreast of events across the country as well as keeping us up to date with relevant research in our field in the Current Research column.We are pleased to present the Consumer Health Information column, three book reviews, and Dean Giustini's column on social media as a tool for recruiting participants for clinical trials; all were capably coordinated by Heather Ganshorn.Product Reviews on Camtasia and Micromedix Carenotes and an update from the Canadian Virtual Health Library complete our final issue of the calendar year.As always, the JCHLA Editorial Team welcomes your contributions, your comments, and your ideas.We'd love
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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 | 0.006 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.019 | 0.015 |
| Insufficient payload (model declined to judge) | 0.240 | 0.155 |
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".