Bibliographic record
Abstract
• Objectives of the researchThis article proposes a new segmentation approach based on social representations and shows the advantages of this criterion compared to those generally used by the profession: desired benefits, socio-demographic and lifestyle.• MethodologyThe authors rely on the theory of social representations and more precisely the conceptual approaches of the central core of Abric and socio-dynamics of Doise. The aim is to search for the most salient elements of social representations and then identify those that best characterize each market segment. Finally, the latter are characterized, ex post, by socio-demographic and behavioural criteria. The application focuses on the bread market with a sample of 570 people.• ResultsThe research leads to identify segments with different representations according to consumption patterns, habits, opinions, desired benefits, but also age, gender and level of consumption.• Managerial/societal implicationsA segmentation by social representations better guides managers in their decision making, particularly on the most salient elements to be taken into account to target each segment. The results show that any action that moves away from the central core is doomed to failure.• OriginalityThe theory of social representations responds better to the main limitations observed during segmentation according to the three main classical criteria used by the profession.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.999 | 0.995 |
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; both teacher heads 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".