What Determines Productivity? Lessons From the Dramatic Recovery of the U.S. and Canadian Iron Ore Industries Following Their Early 1980s Crisis
Bibliographic record
Abstract
Great Lakes iron ore producers had faced no competition from foreign iron ore in the Great Lakes steel market for nearly a century as the 1970s closed.In the early 1980s, as a result of unprecedented developments in the world steel market, Brazilian producers were offering to deliver iron ore to Chicago (the heart of the Great Lakes market) at prices substantially below local iron ore prices.The U.S. and Canadian iron ore industries faced a major crisis that cast doubt on their future.In response to the crisis, these industries dramatically increased productivity.Labor productivity doubled in a few years (whereas it had changed little in the preceding decade).Materials productivity increased by more than half.Capital productivity increased as well.I show that most of the productivity gains were due to changes in work practices.Work practice changes reduced overstaffing and hence increased labor productivity.Changes in work practices, by increasing the fraction of time equipment was in operating mode, also significantly increased materials and capital productivity.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".