Bibliographic record
Abstract
Firms that upgrade and then maintain supply-demand matching collaboration with a highly-governed commercial chain, like a Global Value Chain (GVC), are thought to obtain better opportunities for improving their business prospects. This paper reviews a study on such a hypothetical impact by using data from the fish value chain of Seychelles, comprising a few small-scale producers that have upgraded to supply foreign markets. The difference in the mean value of 5 months’ of production capacity, actual output and productivity (as total output value/input value) of random fish suppliers that had upgraded (n = 34) and not upgraded (n = 32) were tested. Four of the upgraded suppliers were subsequently interviewed on key production-related attributes. Only the difference in the mean productivity figures of the two groups of firms was not significant. The interviews suggest that (1) the productivity of upgraded suppliers is strongly impacted by their directly-controlled resources and exploited fish stocks and (2) viability challenges motivate upgraded suppliers to multi-chain and target various foreign and native customers. The results indicate that supply-demand collaboration in a highly-governed fish chain allows small-scale producers to improve their production capacity, associated output and their potential productivity too if it helps strengthen the environmental sustainability of their fish stocks.
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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 | 0.013 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".