Altruistic and Anomic Suicide: A Durkheimian Analysis of Palestinian Suicide Bombers
Bibliographic record
Abstract
In the past two decades, suicide terrorism in its different forms has become a popular topic of research and debate. It has contributed to a different sense of normalcy and regularity in various societies across the world given that suicide bombings are relatively inexpensive and effective, compared with other kinds of terrorist methods. This study primarily focuses on suicide bombings in the Palestinian/Israeli territories, an area that has experienced conflict and tension for over six decades. In doing so, the research study uses Durkheim’s typology of suicide as a theoretical framework to trace the history of suicide bombings in the Palestinian/Israeli territories, outline the characteristics of suicide bombers, their motivations, and how suicide bombings have been used as a form of resistance to occupation. The data collected cover suicide bombings that have occurred from April 1994 to February 2008. The research study uses logistic regression to examine the characteristics of the suicide bombers and their attacks. The results show, among other things, that the attacks possess elements of both altruistic and anomic types of suicide in the Durkheimian sense of the word.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".