Improving Food Quality through Institutional Innovations: Using a Free-Rider Approach for Collective Action
Bibliographic record
Abstract
This paper applies an innovation from institution economics to agribusiness: profit (cost) sharing as arrangements to promote public good provision. It outlines how a team work approach can be employed to promote food quality. In institutional economics it was recently suggested to use sharing arrangements to overcome the problem of externalities, in particular by using the above mentioned team work approach. Cost sharing as “team work” is analyzed in this paper as a novel institution to improve food quality, then by indirectly creating incentives to overcome the public good character of “quality”. It is assumed that consumers recognize industry quality; not individual by firm in case of asymmetric information. We translate a general approach of negative externality to a positive one. (1) We make a reference to the current state of art on how food quality depends on joint efforts of an industry. The aim is to get a better image and discuss the extent to which group efforts are needed to improve quality. (2) We present a mathematical approach on team work for quality. Quality is seen as positive externality and (3) team building is modelled as a likely a process of forming groups sharing efforts in food industries. Finally some remarks are made on how to actively stimulate the needed process of team formation. Also the role of the government is addressed. At the core of the paper we see the argument that free riding on quality images can be avoided if collective action prevails. A team is modeled as partnership of producers in which costs for image raising are shared. A prerequisite is that economies of scale and jointness in image exist.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".