Balancing Algorithmic Authority and Social Trust : Lessons from CHO Group's Blockchain Integration in Agri-Food Supply Chain
Bibliographic record
Abstract
In a globalized and increasingly complex environment, food supply chains are fraught with information asymmetries, moral hazards, and opportunistic behavior. These issues not only impede collaboration among stakeholders, but also threaten the integrity, transparency, and traceability of food products. The resulting trust deficits not only impact operational efficiency, but also pose significant risks to food safety. This paper explores how blockchain technology, through its potential to establish algorithmic authority - a technology-enabled institutional trust - offers a new perspective on building trust in food supply chains. Our research is based on qualitative data collected from the case of CHO Group, a leading global olive oil producer, and its implementation of the IBM Food Trust blockchain platform. The findings highlight the importance of establishing a “trust equilibrium” within supply chains that balances relational, institutional constructs alongside technological solutions, and reveal that the algorithmic authority of the blockchain is deeply intertwined with existing social trust frameworks.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".