Bibliographic record
Abstract
We would like to acknowledge their contribution as forming a remarkable longitudinal collection of narratives.To the chapter authors, we say "thank you for your engagement, your belief in this longitudinal project, and your aspirational goals, individually and collectively, to 'tell the story' and tell it well."Editing a collection is a dialogue with authors, and they have surely been our keel as we welcomed authors who were new to what is now a series of books.The University of Ottawa Press continues to support us with encouragement, thorough critique, and via ongoing conversations that have resulted in ever deeper and nuanced understandings of both the editing and the publishing process as seen through the lens of a publisher.The press took a risk with us as early-career academics and novice editors back in 2014.Thank you for your willingness to work with us.In a myriad of ways, you have fostered our growth as editors.We proposed this book prior to the pandemic of 2020, but the authors drafted chapters and their work has been edited as the pandemic progressed.The effort represents a phenomenal dedication to this academic endeavour.All of us-editors, authors, publishershave found focus challenging as work routines have been disrupted, with corresponding fluctuations in motivation.Additionally, stark global events have juxtaposed with the analysis of sabbaticals-it surely was an unusual happenstance.As we watch the beginning of vaccine rollouts, we know public-health efforts will improve the global situation, but it will take time.We can only wonder if the full range of sabbatical experiences will return, or if our book will serve
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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 | 0.010 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.404 | 0.266 |
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".