Bibliographic record
Abstract
Positioned at the centre of manufacturing, the steel industry is a key economic sector.Its member companies share changeable financial health, exposure to strongly cyclical demand, vulnerability to oversupply, and a tendency toward price warfare.The most common grades of steel are highly standardized, readily traded, and widely usable.Trade over great distances is encouraged by economies of scale and favourable transportation costs, enabling buyers to enjoy diverse supplies and competitive prices.World steel production grew slowly until this decade, when demand in Asia began to soar.In response, output increased by 58 percent, reaching 1.3 billion tonnes in 2007.More than one-third of that is exported, and 81 percent of net exports come from China, Japan, Ukraine, Russia, and Brazil.With that prodigious volume, misalignment of production and demand can be severely problematic.When there is excess supply, the industry's economics tempt producers to cut prices instead of output.That encourages price warfare, which is a dangerous game in an industry with high fixed costs.Trade makes it possible for overstocks in home markets to be shifted elsewhere and, when local prices require it, to be offered at discount.Prices themselves vary widely -a 337 percent increase in this decade for hot-rolled coil, for example -and economic downturns can lead just as quickly in the opposite direction.All of this makes the industry a contingent milieu, and with most forms of trade protection illegal under WTO rules, producers are on their own.Major steelmakers are consolidating as they search for stability and diversified markets.The unexpected merger in 2006 of the world's two largest, Arcelor and Mittal, portends a massive global consolidation.Soon after that event, Canada's three big producers were acquired by steelmakers from Europe, the United States, and India.Canada and the United States are each other's largest steel suppliers, and the industry's multiple products flow in both directions.Why that trade exists in an active and competitive world market can be explained by proximity, as the two countries' steelmakers are located around the Great Lakes
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.620 | 0.465 |
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".