Towards an EU counter-terrorism policy after the Paris attacks
Bibliographic record
Abstract
L'objecte d'aquesta investigació és l'estudi de les noves mesures que estan essent adoptades a la UE per a la lluita contra el terrorisme, després dels atemptats terroristes comesos a París (al gener de 2015). Pel fet que la UE és un ens supraestatal de caràcter eminentment econòmic més que no pas polític, encara els seus estats membres mantenen les seves competències en matèria de seguretat, deixant a la UE el paper de simple coordinador entre les polítiques de seguretat d'aquests. Tot i això, les reaccions que ha provocat aquest nou atemptat fan preveure que la situació podria canviar als anys vinents. La qüestió és molt important, ja que no només està la nostra seguretat en joc, sinó també els nostres drets fonamentals, els quals entren en conflicte amb algunes de les noves mesures proposades.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.012 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".