Bibliographic record
Abstract
Chapter 4 focused on broad and largely general corporate welfare measures. This chapter examines, in more detail, state provision that benefits individual firms and specific sectors of the economy by, focusing on targeted corporate subsidies and direct company benefits. It draws primarily on WTO data on corporate subsidies that, although patchy, provides a useful glimpse into the range of state support provided to businesses in a number of countries. The first section presents an analysis of the WTO returns for 2006 of seven different countries — the US, the UK, Sweden, France, Germany, Canada and Japan — in order to examine the most important and costliest subsidies provided by these nations and to reveal the key sectors that benefit most from subsidies. The second section examines in detail the full WTO return for one country, the US. This provides fuller evidence of the range of subsidies that are distributed to different sectors. The third section builds on these and other data in order to describe in more detail the kinds of support measures provided by governments to different sectors. The final section looks beyond general business sectors by detailing specific forms of support that accrue to six case-study firms. 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".