Harry Ferns, Bernard Ostry, and The Age of Mackenzie King: Liberal Orthodoxy and its Discontents in the 1950s
Bibliographic record
Abstract
What does an academic article look like? We are all familiar with the basic advice on how to write an essay, how to lay out an introduction, how the body of the essay should follow up on this, provide evidence that is nicely “signposted” throughout, that links back to the argument, and how a conclusion should neatly summarize the material and, ideally, point to its significance. It is all so simple, so clear, so uniform. Historians will occasionally begin with a telling anecdote but this is generally as far as they go in experimentation. Although we tend not to be like social scientists who have explicit “Theory” and “Methodology” sections at the beginning of our papers, we nonetheless follow the same pattern in a more intuitive fashion. Does an academic article have to look this way? The following piece of writing is meant, in part, as a contribution to a debate about how we write as professional historians. This article deliberately takes up a literary style of story telling that is, at present, not respected or at least not followed widely in the profession. The best writerly advice outside of academia has always been “Show, don’t tell.” The writing here is based on this principle. The article aims to be like a novella. The aim is to create a portrait of historical figures and a historical moment that others can recognize and grasp on their own terms. The argument is presented indirectly. The intention is to demonstrate the nature of a historical moment by anecdote, circumstance, and character description. Since the 1960s, historians in Canada and elsewhere have widened the scope of what counts as viable subjects of historical study. The theoretical and political viewpoints deemed acceptable have similarly expanded. We have, presentation / presentation
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.047 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".