Bibliographic record
Abstract
After I had published The Politics of candu Exports in 2006, I felt that I had said everything I had to say on nuclear policy.That changed one day when I was driving with my wife and we heard a news report of a new company investigating the use of nuclear power in Alberta.Teresa turned to me and said, "How many nuclear experts are there in Alberta?"I responded by asking, "You mean outside of this car?"At that moment, I realized that a new development was emerging as several provinces explored expanding their use of nuclear power.I also realized that I was perfectly placed, intellectually and geographically, to write a second book on Canada's nuclear policy.Unlike the first one, this time it would be the domestic story.My pursuit of this story led me to undertake research across Canada: in Saint John, Ottawa, Chalk River, Toronto, Saskatoon, Regina, Peace River, Edmonton, and Calgary.I was also able to interview people from all sides of the nuclear sector: federal and provincial government officials, nuclear industry representatives, scientists, and anti-nuclear activists.The bibliography at the end of this book lists all the people who spoke on the record with me as well as a number of officials who spoke on the condition of anonymity.In addition to these formal interviews, there were even more informal conversations that helped to inform my thinking.I appreciate how willingly people were willing to speak with me, either on the record or off, about nuclear matters, both domestic and international.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.326 | 0.189 |
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".