G<scp>lobal</scp>S<scp>ecurity</scp>P<scp>olicies against</scp>T<scp>errorism and the</scp>F<scp>ree</scp>R<scp>iding</scp>P<scp>roblem</scp>: A<scp>n</scp>E<scp>xperimental</scp>A<scp>pproach</scp>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The World Trade Center attack has shed light on the urgent need to implement preventing measures against terrorism and to enhance cooperation in the global security system for all countries. However, international coordination cannot be taken for granted. It is often ineffective and likely to fail for several reasons. Perhaps the more prominent reason to explain failure in coordination is that collective actions against terrorism may suffer from the well‐known free riding problem. In this paper we experimentally investigate cooperation dilemma in counterterrorism policies by measuring to what extent international deterrence policy may suffer from free riding. In our game, contributions to the group account do not aim to increase the production of the public good but instead seek to decrease the probability that a stochastic event destroys the good. A country could choose to free ride by investing nothing in the international deterrence policy and instead invest all its resources in its own national protection or even choose to ignore totally terrorism by investing on alternative projects. We also look at the effects of institutions that allow sanctioning and rewarding of other countries to facilitate coordination on deterrence policy. We find that, in absence of institutional incentives and after controlling for risk aversion, most of countries defect by investing very weakly in collective actions against terrorism while largely investing to protect themselves. In contrast, the introduction of punishment/reward incentive systems improves significantly the contribution level to the collective security account.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.059 |
| Meta-epidemiology (narrow) | 0.011 | 0.011 |
| Meta-epidemiology (broad) | 0.014 | 0.008 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.018 | 0.010 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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 it