Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Academic, legal and practitioner responses to cyber threats have been predominantly reactive, punitive, and deterrence‐based, with limited attention given to the motives underlying computer criminals' behaviors. This paper reasons that new and better theoretical perspectives are needed to explain computer criminals' motives. Following a review of the computer crime behavioral literature, a summary review of core philosophies and theories used to explain generalized crime and criminal motives is provided. A framework is proposed suggesting that criminological theories have evolved along two categorical dimensions: determinism‐indeterminism, and individualism‐collectivism. The paper then reasons that future computer crime research will benefit by considering indeterminist‐collectivist (constructivist) theories. Two such theories, social construction of technology, and actor‐network theory, are proposed in the discussion section, along with some cybercrime examples. The paper invites a deeper consideration of the origins and motivations of computer‐based criminality as a means of building stronger theory and ultimately advancing more proactive and effective solutions.
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.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it