Bibliographic record
Abstract
Abstract The third chapter of Trade Policy in Multilevel Government introduces the field of procurement as a hard case of trade liberalization. Contracting in line with the principle of ‘best value for money’ curtails public actors’ ability to rely on procurement as a directed means of redistribution. Nevertheless, this principle has served as the rallying cry of an international regime of procurement liberalization that has gradually evolved since the 1970s and whose historical development is described here. In a second step, the chapter elaborates on the patterns of openness and resistance to procurement liberalization among the three multilevel polities chosen for analysis. Over the entire period of observation, the US states’ openness has been comparatively low. Intermittently, their resistance had decreased in the run-up to the 1994 GPA. In recent years, however, the number of states willing to be bound by international procurement disciplines plummeted to virtually zero. As for the Canadian provinces and territories, the picture shifted in recent years. Especially in the negotiations on CETA, they permitted the EU wholesale access to their procurement markets. Within a short period, the Canadian provinces’ position on international procurement liberalization thus witnessed a veritable sea change. Finally, in the EU case, openness on part of member state governments has consistently proved highest among the three cases. Already within the scope of the 1979 GATT Code, all EC members’ central procurement was covered, albeit modestly. In the 1994 GPA and its 2012 revision, the EU covered its procurement on the national, regional, and municipal level.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".