Bibliographic record
Abstract
This book has been under preparation for several years and owes a debt of thanks to many people whom we will identify below.First, however, we want to acknowledge that much has changed in the world and in Canada since we began the preparation of this volume.Sensitivities have changed due to the murder of George Floyd on 25 May 2020 and the global awareness of the Black Lives Matter movement.In addition, the simultaneously devastating impact of the COVID-19 pandemic; the uncovering of the graves of Indigenous children on and near the properties of former residential schools; the burning heat and raging floods in British Columbia, Oregon, Australia, and Turkey; and ongoing earthquakes in Haiti have all raised awareness of inequity, injustice, and racism the world over.The need for action regarding justice and equity is now even more relevant.Were we just beginning work on this book today, our calls for advocacy and action may have been more pressing and our critique of the status quo more acerbic.Indeed, we may have included chapters addressing different issues entirely.Nevertheless, this volume stands as an appeal, at a point in time, for making education at all levels more inclusive, dialogic, and equitable.Moreover, educators must play an important role in promoting understanding of inequity, including awareness of how the relatively privileged majority benefits from, and is complicit in, the suffering of others.Thus, although we recognize the need for much more work, we hope that the present volume will provide a basis for many useful conversations and for transformative action.As the editors of this volume, we find it is important to acknowledge the help and the work of all those who supported us during this publication adventure begun in 2019.First of all, we would like to thank our publisher, the University of Toronto Press, and more specifically Meg Patterson, Acquisitions Editor, for her precious advice and trust.Also, this collective work could not have been realized without the great collaboration of all its authors.We are thankful for their perseverance and hard work to make this project come to fruition.Furthermore, this team work could
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.441 | 0.328 |
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".