Bibliographic record
Abstract
Editing a collection of essays is the art of coordinating moving pieceslots of them.And so we owe a great debt of thanks to our nine contributors-the authors of those moving pieces-who embraced the process of assembling and revising this collection with the utmost in professional generosity.At Wilfrid Laurier University Press, our acquisitions editor Lisa Quinn has shown enthusiasm for this project from the very first, and has handled our queries along the way with good grace and a limitless fund of expertise.Speaking of expertise, we owe the anonymous readers of the manuscript our appreciation for their time, their detailed feedback, their discernment, and their wisdom.This is work that often goes unpaid and unrecognized in our disciplines, and we are deeply grateful for it.A special thank you to P. David Marshall, Chair in New Media, Communication and Cultural Studies in the School of Communication and Creative Arts, Deakin University, for writing such a thoughtful foreword to this book.We thank the copyright owners of the various visual images contained in this volume for their kind consideration and permissions.We are also mindful of the excellent and amiable support we have
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.197 | 0.136 |
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".