Transitions écologique et numérique : quelle régulation du sens par la Commission européenne ?
Bibliographic record
Abstract
Cet article interroge la manière dont la Commission européenne (CE) fait coexister les arguments en faveur de la transition écologique et de la transition numérique, deux priorités stratégiques 2019-2024 difficilement conciliables. Aussi intimement liées sur le fond que séparées sur la forme, les transitions écologique et numérique font l’objet de deux rubriques distinctes sur le portail web de la CE : elles constituent le corpus de l’étude, multimodal et riche d’un point de vue éditorial. Après avoir situé notre démarche au sein des approches théoriques de la communication publique européenne et élaboré la méthodologie selon une approche discursive et sémiotique, nous confrontons les résultats de l’analyse du discours à ceux de l’étude des paysages textuels (textscapes). Les filiations discursives, les tensions, les variants (motifs, récits, modèles proposés ; lieux et moment signifiants) et les invariants (énoncés gnomiques) repérés permettent de préciser la doxa sur laquelle repose le paradoxe communicationnel considéré.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".