Half-Century of Stagnation: Labor Productivity in Ontario’s Gold Mining Industry
Bibliographic record
Abstract
This paper explores labor productivity in Ontario’s gold mining industry from 1920 to 1970. The gold produced by a worker is nearly identical in 1920 and 1970, suggesting that the industry experiences no productivity gains over this period. Further, labor productivity in the intervening years was nearly 30% lower than these values, raising concerns about the ability of the industry to remain profitable given a fixed gold price. We look at over 180 different Ontario gold mines comprising nearly the entire industry to determine whether workers become less efficient over time, or whether other factors, such as entry and exit into the industry, declining ore quality, or changes in capital stock, are the primary drivers of this stagnation. This analysis considers the impact of events, such as a sudden 70% rise in the price of gold in 1934, World War II, and the post-war subsidization of the industry on productivity within the industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".