Bibliographic record
Abstract
Column Editor's NotesAt any stage of the professional life cycle (early, mid, or late career) we all have experiences of leadership, be they good and/or bad. This feature column, written by Vicki Whitmell (Canadian founder of the Library Leaders Institute—LLI) provides for the record a very personal account of taking an opportunity to lead from where she stands and give to others a positive and supportive leadership learning opportunity.John Fitzgerald Kennedy, the 35th president of the United States of America, in his inauguration speech in 1961, made an impassioned and memorable call to action for the nation to do what is right for the greater good. His words, slightly modified with reference to your profession not your country resonate as I read and reflect on Whitmell's account of the LLI. Her description, observations, and reflections provide a very personal account of her contributions for the greater good of the library profession, through the LLI.Through the focus of this column, I am pleased to bring this story of the evolution of the LLI to the professional literature and to the attention of our international readership. Publication of the LLI story provides acknowledgement of the personal and professional commitment of the LLI founder and helps spread the word about what individuals and the profession have gained from the LLI. It also provides a blueprint for other interested in leadership development. Having personally observed the learning experiences of one LLI cohort, I know this innovative and professionally credible program has brought benefits for individuals and the broader library community, especially in Canada.As always, I invite contributions to the column on topics broadly addressing themes or issues for library workers, throughout their career lifecycle. Please submit articles for this column to the editor at vicki.williamson@usask.ca
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.029 | 0.019 |
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".