Industry Case Studies: Steel, Biofuel Production, Semiconductors, Automobiles, Call Centers
Bibliographic record
Abstract
This chapter will present case studies of incentives in five industries. The first concerns the steel industry. Classic locational tournaments have characterized the announcement of new plants, while during the current recession, companies have been asking for subsidies to avert shutdowns. The second case examines the use of incentives to biofuel production facilities (note: not general subsidies to biofuels) in a number of countries throughout the world. The third case is microchip fabrication, which pits several locations in the US and Europe against developing countries like Singapore, China and soon, India. Fourth, there will be a brief recounting of selected bidding wars for auto facilities in both the industrialized and developing world. Finally, we analyze a much less capital intensive sector, the call center industry. It will focus in particular on the use of incentives in the spread of this industry to India, South Africa, the Philippines, parts of the Caribbean and Canada, as well as some cases in the US where the use of incentives have been credited with a decision in their favor over competing jurisdictions in India. In all of these cases, there is no way to be exhaustive; instead, limitations of space and information availability play a role. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".