Semantic Hacking and Intelligence and Security Informatics (Extended Abstract).
Bibliographic record
Abstract
In the context of information warfare Libicki first characterized at- tacks on computer systems as being physical, syntactic, and semantic, where software agents were misled by an adversary's misinformation (1). Recently cognitive hacking was defined as an attack directed at the mind of the user of a computer system (2). Countermeasures against cognitive and semantic attacks are expected to play an important role in a new science of intelligence and secu- rity informatics. Information retrieval, or document retrieval, developed histori- cally to serve the needs of scientists and legal researchers, among others. In these domains, documents are expected to be honest representations of attempts to discover scientific truths, or to make sound legal arguments. This assumption does not hold for intelligence and security informatics. Intelligence and security informatics will be supported by data mining, visuali- zation, and link analysis technology, but intelligence and security analysts should also be provided with an analysis environment supporting mixed- initiative, utility-theoretic interaction with both raw and aggregated data. This environment should include toolkits of semantic hacking countermeasures. For example, faced with a potentially deceptive news item, an automated counter- measure might provide an alert using adaptive fraud detection algorithms (3), or through a retrieval mechanism allow the analyst to quickly assemble and ana- lyze related documents bearing on the potential misinformation. The author is currently developing such countermeasures.
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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 0.009 |
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".