Bibliographic record
Abstract
The issue of international terrorism at the beginning of the 21st century, in the United States in particular, has left populations asking: “who and where is the enemy and how do we bring them to justice?” But a more important question could be “Who is the victim and who is not, and what measures are appropriate while minimising collateral damage?” Peeling away the complex layers in the politics of anti‐terror policy leads to the inevitable conclusion that long gone are the alleged days of “black and white” cases of nation‐wide warfare. Franz Kafka all too often warns of the psychosocial pressure that follows as a result of the hegemonic power dynamic, and though imperialism was no new concept in Kafka’s era, his concerns shine ever brighter in light of the rise of the neo‐colonial anti‐terror initiative. Politics aside, this situation is a powerful echo, prophecy even, of a host of Kafka’s literary works, which warn of a ‘psycho‐dystopian’ world of torture machines on colony‐island penitentiaries, of summary executions and the breaching of basic human rights to achieve a government’s desired end. In this presentation, I will demonstrate how Der Prozeβ (The Trial) – with its parallels to the ethereal nature of law‐ is still relevant to the case of the U.S.‐led “War on Terror” in a world of liberally accessible media and information, spreading discontent and paranoia in the hearts and minds of the very population that the anti‐terror policy is designed to “protect”.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".