Bibliographic record
Abstract
The world is bedevilled by terrorism. Of the most recent treats and challenges that the world faces there seem to be none bigger than terrorism. There is hardly a week that passes when you do not hear of terrorist attacks in one place of the order. Terrorist attacks have been perpetuated in places like Syria, Iraq, Afghanistan, Nigeria, Somalia, Yemen, and Pakistan, the United States, Britain, Canada, France, Italy and so forth. The effects and impact of terrorism are incalculable. Thousands of lives and properties have been lost to terrorism. If terrorism is not mitigated or stopped it has the capacity to plunge the world into a new dark age. The paper examines the reality, effects, and causes of terrorism. It uses critical analytic and evaluative methods to examine terrorism from the lens of moral cosmopolitanism. The paper proposes that the idea of moral cosmopolitanism that affirms the common humanity of all humans and obligates each human to come to the help of others, even strangers can help to combat global terrorism. The paper finds that there is need to educate, conscientize, and persuade global terrorists with the value of moral cosmopolitanism. If this can be done it will greatly help in mitigating global terrorist attacks.
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 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.041 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".