Nature's Past Episode 028: Winnipeg Beach
Bibliographic record
Abstract
In the late decades of the nineteenth century, urban North Americans sought refuge from congestion, noise, and pollution. As the environmental problems of industrial cities grew worse, city councils across the continent established urban parks while federal governments in both Canada and the United States developed national parks systems. Parks, as constructed natural spaces, were just one option for city-dwellers seeking relief from polluted urban environments. Many flocked to the shores of oceans, lakes, and rivers where beach side resorts emerged as yet another recreational destination. \n \nAt the beginning of the twentieth century, Winnipeggers turned to the shores of Lake Winnipeg to the north of the city in the hopes of finding an outlet for their leisure time. There the Canadian Pacific Railway established the beachside resort community of Winnipeg Beach. For more than half a century, Winnipeg Beach was one of the most popular recreational retreats for Manitoba’s urban population. Thousands of people enjoyed the lake views and boardwalk entertainments of Winnipeg Beach for many years until the community went into decline by the end of the 1960s. \n \nOn this episode of the podcast, we discuss the history of Winnipeg Beach with author, Dale Barbour. \n \nPlease be sure to take a moment to fill out a short listener survey here.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.012 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.100 | 0.014 |
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".