A Science of Evil: An Exploration into Terror Management Theory, and a Psychoanalytic Theory of Extremism
Bibliographic record
Abstract
Terror Management theory is a refinement on Psychoanalytic theory that places the knowledge and resulting fear of mortality as the primary motivating factor in human behaviour. Based largely on the work of cultural anthropologist Ernest Becker, the theory seeks to examine human nature from an existential standpoint, and use psychoanalytic observation to create a comprehensive theory of the subconcious factors that comprise human behaviour. This paper seeks to provide an introduction and general explanation of the essential premise of Terror Management Theory, and explain in detail one of the most integral aspects of the theory, the projection of death-anxiety from an individual onto a person, object, or abstraction, known in TMT as transference. The ideas developed in the first part of the paper are then used to develop an existentialist psychoanalytic rationale behind the extremist behaviour of the radical Islamic terrorist organisation Al Qa'ida. The paper concludes by conducting a brief review of the scientific research studies that have in the past few decades succeeded in providing solid experimental data that supports the predictions made by Terror Managment Theory.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.048 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".