Bibliographic record
Abstract
State Trading Enterprises (STEs) are one of the bete noirs of agricultural and other trade relations and trade negotiations. An STE is a government enterprises or quasi government enterprise that operates with special protections and/or privileges granted by its country’s central authority. STEs generally exist for one of two main reasons. Sometimes, as with many African parastatals, they are created to tax the domestic industry and/or imports for government revenue generation purposes (or income transfers to members of ruling elites). Alternatively, an STE’s mission is often to increase revenues or profits (though not necessarily both) from sales for domestic producers and/or processors and other marketing chain operations. In pursuing these revenue or profit objectives, STEs create trade distortions by implicitly levying tariffs on imports, taxing domestic sales, and subsidizing (or, on rare occasions, taxing) exports to different countries at different rates. They may also be vehicles through which domestic subsidies are more or less discretely funneled to producers, with corresponding implications for the effectiveness of disciplines on domestic supports. Hence, STEs are problematic in the context of trade negotiations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.013 | 0.006 |
| Insufficient payload (model declined to judge) | 0.398 | 0.211 |
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".