Bibliographic record
Abstract
FOR A VARIETY of reasons, I approached Canada: A People’s History gingerly. I value the CBC and I did not want it to fail in this outrageously ambitious venture and the likelihood of failure, in my opinion, was high. As the co-author of a Canadian history textbook, I am painfully aware of how hard it is to reconstruct even a brief episode in our past, let alone the whole sweep from beginning to end. What being a textbook author means for this exercise is that I know too much. I have experienced the difficulties of getting the history of Canada right for an educated audience, and I have suffered the slings and arrows of an impressive array of critics who complain about errors of fact, imbalance in content, and bias in interpretation. Fortunately, the textbook has gone into second and third editions and much has been done to correct errors and omissions pointed out to us. No one, of course, ever refers to subsequent editions. Once the tone and focus of the criticism is set, it takes on a life of its own. I therefore have only the deepest sympathy for Mark Starowicz and his production team who are experiencing the thousand cuts from academic critics, most of whom tend to repeat each other, but who have never tried to produce history on television themselves. I should also acknowledge that I am currently developing a course called Canada on Film, which means that I am deeply immersed in the academic literature in the field of historical film. Even in my sleep I can chant Robert A. Rosenstones mantra: A film is not a book. An image is not a word. I
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.144 | 0.018 |
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".