Could there be Unintended Effects of Government Support for Seafood Traceability Implementation on Business Planning? Results of a Survey among Italian Fishery Businesses
Bibliographic record
Abstract
Governments have intervened in food, agricultural and fisheries markets through various support programs to promote adoption of traceability practices and systems in order to raise food safety levels and increase industry competitiveness. The aim of this paper is to investigate intended and unintended effects of participation in such supporting programs. Intended effects comprise of the impacts on traceability capacity levels, costs and benefits of program participants vs. comparable non-participants. Unintended effects concern the firms’ planning accuracy which we propose to measure through deviations of actual from expected outcomes. We conduct our empirical analysis based on a sample of 55 Italian fishery businesses which we divide in firms who received support, a comparable control group and the remaining sample. Although we find that recipients of government support have higher average levels of traceability capacity and overall benefits than the control group, differences are not statistically significant. In regards to the unintended effects of government support, we find that recipients of government support reported larger deviations of actual from expected benefits than the control group did. While these differences were not significant at the aggregate level, significant differences are found at the level of specific benefit categories. For example, support recipients had overestimated sales and price related benefits but severely underestimated efficiency gains in operations. The results suggest that the motivation for participating in a government support program may not align with the firm’s strategic goals. This misalignment may reduce planning accuracy.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".