A Decade of Media Coverage of the Social Reintegration of Terrorism-Related Convicts: France as a Case Study
Bibliographic record
Abstract
The social reintegration of terrorism-related convicts in Europe is a pressing issue. Public opinion can play an essential role in this by making it easier or more difficult to implement (and succeed with) social reintegration strategies. Considering the media’s influence on shaping public opinions, attitudes, and social representations, the present research offers a case study by reviewing a decade (2011–2022) of media coverage of the social reintegration of terrorism-related convicts in the seven most read national daily newspapers in France. Results reveal that the topic is very little covered, with 395 newspaper articles published over a decade, and mostly discussing deradicalization, specifically, rather than social reintegration at large. Cluster analysis via Reinert’s method reveals that when the topic is discussed it revolves around political and security management (political discourse and security measures), target population (radical Muslims and returnees), and tertiary prevention programs (programs in prison and open settings). A time series analysis of clusters shows their chronological evolution. These findings and their implications for generating (mis)trust in the social reintegration of terrorism-related convicts amongst the general public are discussed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".