Bibliographic record
Abstract
T his book is the culmination of research begun when I arrived at theUniversité du Québec à Trois-Rivières in winter 2002.What was originally conceived as a project focusing on the history and socio-politics of science changed and expanded following my graduate work on the autonomy of governmental scientific institutions.For a long time, I was interested in the history of the environment.I wanted to understand how nature was shaped by governmental scientific activity and, conversely, how the state's knowledge and power provided the basis for the potential for natural resources to be invented and inventoried, discovered and extracted as national assets.In this regard, my exchanges with my colleagues from the Network in Canadian History and Environment (NiCHE) were very stimulating.I would like to thank, in particular, my colleagues Matthew Evenden and Laura Cameron, as well as Liza Piper, for their encouragement.During scholarly events organized under the auspices of NiCHE, I had an opportunity to collaborate with geographers, from both Quebec and elsewhere, and to draw my most recent inspirations.As I looked more closely at historical and political geography, I sought to comprehend how the spatial dimension of human activity, both intellectual and practical, became an integral part of the environment I was studying.As I made these detours, I benefited from the contributions of a generous group of people.I have to mention first the research professionals at the Centre interuniversitaire d'études québécoises (CIÉQ), to whom I owe a great deal, including the final quality of the composition of this book:
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.360 | 0.204 |
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".