Law and Order in the Arctic: “The Smiling People” and <i>RCMP</i>
Bibliographic record
Abstract
Since the 19th century, Canadian culture has been rife with Mountie lore, and since the 1970s, many critics have deconstructed the Mountie myth, showing how this police force was romanticized in both non-fiction and fiction alike. This paper explores one example of such cultural mythmaking: a fictional television script about law and order involving Indigenous and non-Indigenous peoples in the Canadian North. “The Smiling People” was one episode in the 1959 television series, RCMP, produced by Crawley films, the BBC, and CBC, and shown in Canada as well as around the world. The RCMP series featured a small-town detachment of three RCMP men in northern Saskatchewan who sometimes ventured further north to bring their version of policing and justice to both whites and Aboriginal peoples alike. In the case of “The Smiling People,” an Inuit woman is charged with the murder of her husband; the episode covers the unfolding trial as the Mountie hero uncovers the truth about the murder. “The Smiling People” offers a story about the “contact zone” of white and Inuit which features the clash of cultures, the imposition of white values, yet the paternalistic, and ultimately ‘superior’ knowledge claims of white, southern legal practices. As such, it is an excellent example of the emerging cultural justifications for colonial consolidation in the North in the post-World War II period.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.038 | 0.028 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".