Ethical Branding in the Modern Retail: A Comparison of Italy and UK Ethical Coffee Branding Strategies
Bibliographic record
Abstract
Modern markets can be seen as complex systems of relationships where stakeholders are able to influence the firms’ decision-making processes and their value creation processes. Modern businesses should adopt a broader perspective in order to not focus their actions only on maximize economic performance, but to design them considering even their social and environmental impacts on the system as a whole. Firms have to respond to the stakeholders’ expectations as a way to obtain the legitimacy needed to create a beneficial environment that will help them in creating a positive effect out of their system of relations, without violating the social contract tying together all the actors in a given system. It follows that, when companies can effectively communicate to their systems’ actors how they are following the principles of sustainability and prove that their actions are socially responsible, they can get several advantages. One of the way companies must accomplish this feat is to ask third parties to certify their actions in order to be able to print on their products one of the various Ethical Labels. Using these labels to mark their products can be a tool to influence the consumer to buy from the firm over the competitors, leveraging on a higher legitimacy. In this paper, we have studied the evolution of the practice of non-financial disclosure trough ethical labels that 14 coffee brands, both in Italy and in England, as a way to understand how, in different markets they have changed over a 5-year time.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".