Bibliographic record
Abstract
• Research objectives/topicsThe purpose of this research is to understand the processes of delegitimation and relegitimation applicable to a retail format, taking the example of large-scale food retailing (LFR), whose legitimacy has regularly been questioned in recent years.• Methodology/approachWe draw on and compare the theoretical frameworks of the economies of worth and of the neo-institutional approach and use them to put forward an original approach based on the notion of legitimacy test(s), which enables us to analyse a number of semi-structured interviews conducted with purchasers who buy their food from large retailers and formats normally associated with local supply chains.• ResultsCriticism emanating from the domestic world and seeking to delegitimise the LFR sector’s industrial-market compromise is being internalised by the latter in order to relegitimise itself, in particular by incorporating local supply chains. Nevertheless, the legitimacy test applicable to this action in the LFR sector’s initial compromise is not proving successful with purchasers who are overwhelmingly opting for local supply chains.• Managerial/societal implicationsWe explore the possibilities of a new compromise that would enable the LFR sector to internalise criticism by moving beyond the tension between heritage and technical skills, and “focusing on the local”. We also offer our thoughts on the changes that might occur in food retailing between now and 2025, both for the LFR sector and for local producers.• OriginalityThis research follows on from other research projects undertaken in recent years on the subject of legitimacy and puts forward an original framework for analysing the legitimation processes applicable to a retail format.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.930 | 0.918 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".