Bibliographic record
Abstract
Tanzania’s cotton conundrum will not simply be resolved through the reduction and eventual elimination of the manifold protections the US government affords King Cotton. While the economic, ethical, moral and social rationales for a prompt exorcism of this external bogeyman have been articulated and disseminated for years, and trade watchers around the globe continue to back these grounds for policy change (see Stiglitz 2006b), the removal of US subsidies cannot be a silver bullet. Those that uphold the idea that a freer cotton trade will necessarily constitute a quick win for impoverished cotton growers maintain a position that ironically parallels the rigid and determinist stance radical critics of capitalism took during the 1970s. Then, dependency theorists focused narrowly on the ways that imported governance institutions and asymmetrical linkages with the world capitalist economy fostered underdevelopment in peripheral zones such as tropical Africa (Leys 1975). Africanist scholars who underscored the inapplicability of monocausal explanations south of the Sahara later took the unidirectional dependency perspective to task (Berman 1992). 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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".