La consommation collaborative en Europe : Démêler la dualité des rôles
Bibliographic record
Abstract
Cet article apporte de nouvelles preuves sur l’économie collaborative en Europe grâce à l’analyse des motivations à participer à une plateforme collaborative en tant qu’acquéreur ou fournisseur. À cet effet, nous analysons un échantillon paneuropéen de 14 050 citoyens provenant de 28 pays. L’étude, qui applique une méthodologie de prévision empirique grâce à un modèle d’équations structurelles, fournit deux principales contributions à la littérature. Premièrement, les motivations économiques et les motivations basées sur l’efficacité prédisent l’acquisition et la fourniture de biens et services sur les plateformes collaboratives en Europe. Deuxièmement, les échanges non financiers prédisent également la fourniture sur les plateformes collaboratives. Nos résultats ont également des incidences sur l’aménagement du territoire. Comprendre les motivations entre les acquéreurs et les fournisseurs peut favoriser les échanges collaboratifs des ressources essentielles, plus particulièrement à petite échelle et à l’échelle locale.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".