Marlene Wind efterlyser selvransagelse: Medierne spåede igen forkert. Denne gang om Brexit og Trumps effekt på Europa
Bibliographic record
Abstract
Torsdag d. 11 maj, var centerleder og professor ved Center for Europæisk Politik aktuel med en kronik i Politiken med titlen: ”Marlene Wind efterlyser selvransagelse: Medierne spåede igen forkert. Denne gang om Brexit og Trumps effekt på Europa”. Professorens indspark i debatten går på, at medierne allerede inden valget havde en ’antielitedagsorden’, og valget af Emmanuel Macron derfor bør henlede til selvransagelse i det danske medielandskab. ”Selvransagelsen burde derfor være lige så stor efter valget af Macron som den, mediefolk opviste efter valget af Trump og Brexit, da de sendte deres bedste korrespondenter til de mest vindblæste udkantsområder i USA. Folket skulle høre om stort og småt.” Marlene Winds anke mod mediernes dagsorden, er at det ikke var et antielite valg som spået. Valget af Macron var nemlig på baggrund af en pro-EU dagsorden. Dog betyder det ifølge professoren ikke, at alt er fryd og gammen. ”Betyder det så, at alt er fryd og gammen? Nej, selvfølgelig ikke. Der er stadig mange utilfredse franske vælgere. Både dem, der stemte på Le Pen, og dem, der stemte på Melenchon.”
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.107 | 0.028 |
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".