Bibliographic record
Abstract
The language of influence or propaganda has been studied for a century but its predictions (simplification, deceptiveness, manipulation) can now be examined empirically using corpus analytics. Semantic models for intensity of belief and use of gamification as a strategy allow novel aspects of influence to be taken into account as well. We develop a semi-automated approach to assess the quality of the language of influence using semantic models, and singular value decomposition as a middle ground between high-level abstract analysis and simple word counting. We then apply this approach in a significant intelligence application: examining the use of the language of influence in the jihadist magazines Inspire and Azan . These magazines have attracted attention from intelligence organizations because of their avowed goal of motivating lone-wolf attacks in Western countries. Our approach enables us to address questions like: How good are the authors and editors of these magazines at producing influential language (and so how great is the impact of these magazines likely to be)? How does this change with time, and as a reaction to world events, and what does this tell us about competence and strategic goals? What is the impact of changes in authorship?
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 | 0.001 |
| 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".