Bibliographic record
Abstract
This book has emerged from a collaboration between the two authors that spans three decades.Russell Craig is grateful for the privilege of working with a co-author of vibrant intellect and a seemingly unbounded tolerance and patience.Joel Amernic is grateful for the privilege of working with a co-author of sterling creativity and dedication to the highest standards of research.Both authors are mutually grateful for the warm bonhomie that has sustained their longstanding collaboration.We thank our wives (Annette Craig and Lilly Amernic) and our extended families for their patience and tolerance as we labored on this book, distracted, often at odd hours.We also thank our respective universities (Faculty of Business, Durham University, UK, and the Rotman School of Management, University of Toronto, Canada) for providing the facilitative research environments that have enabled us to complete this book.We are grateful also to Jennifer DiDomenico of the University of Toronto Press (UTP) for her skill, patience, and good counsel in guiding this book through UTP's rigorous approval process.We have benefited too from the perceptive feedback provided by Jessica Ross related to social media and from many helpful suggestions from the copy editor, Susan Bindernagel.Parts of the following chapters have been sourced in material alluded to, or adapted and extended from, joint scholarly papers we have published between 2004 and 2020.In the list below, we acknowledge any additional authors of those papers in parentheses.Thus, we express thanks for the collaborative contributions of Dennis Tourish, Tony Mortensen, Shefali Iyer, Rebecca Nicolaides, Richard Trafford, and Rofiat Alli.In addition to calling upon the "back catalog" of our scholarly publications that are listed below, this book also comprises much new material that has not been published previously.We also canvass "breaking issues"
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.334 | 0.247 |
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".