Bibliographic record
Abstract
Of all the words that fill the pages of this book, none are more important or meaningful to me than the ones penned below.In the course of researching and writing Northern Lights, I have benefitted greatly from the support, guidance, and wisdom of many people, and I hope that in the few paragraphs that follow, I can adequately express my gratitude.First and foremost, I would like to thank Philip Cercone, executive director of McGill-Queen's University Press, and his colleagues, for guiding my book through the publication process.Philip has been a strong and committed advocate of my work for many years, and I cannot thank him enough for all he has done to showcase my research.I would also like to thank Gillian Scobie for the superb job she did in copy-editing my manuscript and Anna Zuschlag for being so meticulous in preparing the index.I would also like to thank the anonymous reviewers for their thoughtful and insightful comments.Their efforts on my behalf are greatly appreciated.Any omissions or errors rest entirely with me.The idea of writing a book on Canadian think tanks had been on my mind for many years, but it was not until the fall of 2014 that I sat down to draft this manuscript.As a visiting professor at Université Aix-Marseilles in Aix-en-Provence, and later at Sciences Po Lyon, I found the ideal environment in which to reflect on this important subject.Greeted every morning with the smell of freshly baked baguettes and piping hot café au lait, my creative juices began to flow.After spending several hours each day reflecting on why writing from France about Canadian think tanks made perfect sense to me, I found myself strolling along ancient cobblestone streets, listening to the beautiful sound and rhythm of church bells, and watching people engage in lively conversation at outdoor cafés.It did not take long for me to understand why so many people begin a new
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.240 | 0.186 |
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".