Bibliographic record
Abstract
This project had its genesis at a conference on New Brunswick politics organized by Bill Cross at Mount Allison University in October 2000.A joint paper given there, which grew out of David's doctoral work, engaged our shared interest in Maritime politics, highlighted the extraordinarily rich data that existed on party leadership elections in the region, and suggested a long-term project that might pull this data together.We extend our appreciation to Bill for organizing the conference, to those who attended for their insights, and to Ken Carty who provided encouragement and continued to challenge us with his valuable feedback along the way.We owe a real debt of thanks to Agar Adamson and March Conley, who, during their years at Acadia, began this project and kept it going for a number of years.Ian Stewart continued this work after they had moved on to other projects.We also thank Bill Cross, who co-administered and co-financed two of the later surveys.Funding a project over such a long period is challenging, but Acadia University was most supportive with regard to providing financial assistance, and, towards the end, assistance was also provided by the University of Manitoba.We thank those institutions for their support.In particular, we wish to thank Suzanne Stewart and Danielle Fraser at Acadia for their administrative support and Richard Sigurdson at the University of Manitoba for providing David with time to work on this project.Thanks also to the students who provided research assistance (particularly Kiley Thompson and Steven Hobbs at the University of Calgary).Versions of Chapters 4, 7, and 9 were presented at annual meetings of the Atlantic Provinces Political Studies Association, and we thank that organization for its support of research on the region and, more directly, the discussants who provided us with valuable feedback.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.370 | 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; 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".