Bibliographic record
Abstract
L’objectif de l’article est de déterminer si la communication de la BCE a changé quantitativement et qualitativement durant la crise. Une comparaison internationale est faite avec la Fed et la Banque d’Angleterre afin de déterminer si la BCE se distingue en la matière.La méthodologie employée est celle de la littérature de la communication des banques centrales, en particulier l’approche de wording. Un des apports de l’article à cette littérature est d’étudier un spectre large de moyens de communication des banques centrales : non seulement les discours des banquiers centraux, mais aussi les conférences de presse, le dialogue monétaire de la BCE au Parlement européen, ainsi que de multiples publications de la BCE. L’article étudie ses communications de la BCE de 1999 à décembre 2014. Il contribue aussi à la littérature des comités de politique monétaire, notamment sur l’importance du « chairman ». Trois résultats majeurs sont mis en évidence : la crise a modifié qualitativement et quantitativement la communication de la BCE, les communications des présidents de la BCE sont quantitativement et qualitativement différentes, l’autorité monétaire de Francfort est moins communicante sur les thèmes liés au chômage, mais plus communicante sur la stabilité financière.
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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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; 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".