Les voies d'avenir du magasin physique à l'heure du commerce connecté
Bibliographic record
Abstract
Face à l’essor du commerce électronique (e-commerce) et à l’émergence du commerce mobile (m-commerce) qui bouleversent le secteur du commerce de détail, le magasin physique a-t-il un avenir ? La réponse semble positive : si le magasin physique traditionnel est remis en cause, il est moins appelé à disparaître qu’à être réinventé. L’article présente les quatre défis majeurs auxquels le magasin physique fait face ainsi que les cinq atouts clés dont il peut se prévaloir. Puis, nous appuyant sur des exemples nord-américains, asiatiques et européens, nous analysons cinq voies d’avenir : le magasin physique de l’avenir devrait être éphémère, tiers-lieu, vitrine, digitalisé ou virtuel. Enfin, nous présentons les implications à maîtriser pour une transformation réussie du magasin physique.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".