Combating terrorism by constraining charities? Charity and counter‐terrorism legislation before and after 9/11
Bibliographic record
Abstract
How does counter‐terrorism legislation – enacted in democratic states – impact upon charities, intentionally or unintentionally? To address this question, we present a new analytical framework that allows us to compare, across established democracies, how charity legislation and counter‐terrorism legislation are connected, enabling us to assess how charities’ legal environments have changed since 9/11. Comparing legislation across six long‐lived democracies (theUK,US, Australia, New Zealand, Canada and Ireland), we distinguish between three types of legislative connection: overlap, direct intersection and indirect intersection. These categories differ in terms of the visibility of the connection established between the two areas of law. As high‐profile reform exercises, both overlap and direct intersections have been predominantly introduced post‐9/11. But it is through indirect intersections that intensified post‐9/11 which are most vague and difficult to manoeuvre, that the day‐to‐day activities of charities are most likely to be affected, with important empirical and normative repercussions.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".