Bibliographic record
Abstract
The global commodity chains (GCCs) approach is an insightful way to understand issues of `development' and production and consumption differentials across space. It potentially offers insight into the issue of `ecologically unequal exchange'. However, we propose three revisions to conventional GCC analysis. First, many of the GCC studies tend to focus on only part of the commodity chain — and we need, in effect, to `lengthen' the chains. Stephen Bunker (1984) emphasized that `commodities can emerge only from locally based extractive and productive systems' (p. 1017). Beginning GCC analysis with these primary products forces the examination of various modes, techniques and technologies of extractive regimes, as well as the key roles of transportation and communications systems. Second, focusing on this `longer chain' requires analysis of spatially based disarticulations and contestations. Mineral deposits and agricultural economies tend to be tied to specific `natural' geographies — thus, `enclave economies' frequently develop that are globally integrated but locally disarticulated. Transportation systems (especially of bulk products) are extremely vulnerable to disruption and change dramatically over time. Third, we explicitly focus on tightly integrated social and natural processes across a range of industries. The goal is to focus on the relationship between long-term changes in the world economy and the natural environment, as well as on research in environmental studies that examines `ecologically unequal exchange' and points to prospects for sustainable development.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.015 | 0.032 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".