Bibliographic record
Abstract
The term terrorism is as value-laden a descriptor as one will encounter in the contemporary period. Though it evokes a strong image of an Orientalist, colonized, brown body enacting brutal, theatrical violence from behind a balaclava, the term itself describes very little. The decision to label a particular act, individual, or movement as terroristic is more a discursive question of politics than means. In the post-9/11 era, state-level rhetoricians describe their ideological enemies that can be “othered” as terrorists, while some are considered extremists. In doing so, Muslim, Arab, Asian, African, and foreign-born advocates and practitioners of political violence are termed terrorists with near universality, while white, Christian, Westerners acting in the name of white supremacy, anti-abortion, and so-called patriot, or sovereign citizen movements are left largely outside of that taxonomy. Through an analysis of the film Black Hawk Down, jihadist-produced media designed for US audiences, media accounts of Boko Haram in Nigeria, and the framing of rightist violence, it is clear how violence is viewed positionally. Furthermore, these examples demonstrate how terrorism has been utilized as a defamatory label applied asymmetrically to some proponents of political violence—those brown and black lives existing in precarity who challenge discursive claims on violence, statehood, capital, and what are broadly understood to be Western values.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".