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Record W2546286028 · doi:10.5539/jpl.v9n9p65

Jordan Role in Compacting International Terrorism

2016· article· en· W2546286028 on OpenAlexvenueno aff
Ziad M. Rabie

Bibliographic record

VenueJournal of Politics and Law · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsnot available
Fundersnot available
KeywordsTerrorismClass (philosophy)Political scienceLawArabicPoint (geometry)Computer scienceMathematics

Abstract

fetched live from OpenAlex

Many Arabic countries are suffering from big waves of International terrorism, to a point that it requires from all countries to unify their efforts in compacting the international terrorism; Jordan in other hand had played a great role in compacting it, and as a result many laws had been issued in Jordan criminalizing such terrorist acts. Not only that, but Jordan had joined the international coalition to compact the International terrorism effectively; as a result, the Jordanian forces are participating effectively in targeting many terrorist locations around the world, on the other hand it was targeted by many terrorist attacks resulted in many deaths and casualties among Jordanian citizens. In this research I addressed the International terrorism concept and the laws issued in Jordan to compact terrorism, in addition pinpointing the acts that are considered terrorist acts. the research ended with a conclusion that include the most important recommendations from the restate point of view.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.006
Scholarly communication0.0090.005
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.018
GPT teacher head0.320
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2016
Admission routes1
Has abstractyes

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Same venueJournal of Politics and LawSame topicTerrorism, Counterterrorism, and Political ViolenceFrench-language works237,207