Bibliographic record
Abstract
This book, like most academic publications, is the result of several years of reflection and (sometimes unconscious) preparation, several more years of research, several months of intensive writing, and a lengthy period of waiting for the comments of readers on the (more or less) finished product.During each stage, a number of obligations were incurred.First and foremost, I am grateful to the Social Sciences and Humanities Research Council of Canada for funding this research under grant no.410-99-0519.Its support enabled me to make several visits to Ireland, a country of which I had no previous experience, between 1999 and 2003, as well as shorter visits to archives in Ottawa and Quebec, and to hire student research assistants for three years.I am grateful also to the Irish academics who took an interest in this project and provided me with hospitality and advice, including Tom Garvin and John Coakley of University College Dublin, Michael Marsh of Trinity College Dublin, and Richard English of Queen's University Belfast.They helped to make my visits to their great country both enjoyable and productive: so much so that I am now planning to embark on a second Irish research project once the present one is published.Brock University in St Catharines, Ontario, which has transformed itself in recent years from a small, primarily undergraduate teaching institution to a medium-sized, research-oriented university, has provided me with a congenial working environment since 1987, and specifically during the period in which I worked on this book.In particular, I would like to thank my student research assistants at Brock:
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.330 | 0.157 |
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".