Bibliographic record
Abstract
Los estudiosos de las transformaciones que la revolucion digital ha provocado en la delincuencia subestiman a menudo el dilema de la confianza al que se enfrentan los delincuentes en linea. Sin embargo, en un contexto de necesaria convergencia de conocimientos tecnicos y organizativos para llevar a cabo proyectos lucrativos, los vinculos de confianza desempenan un papel decisivo en la eliminacion de socios de dudosa fiabilidad y en la estabilizacion de colaboraciones para mejorar el rendimiento delictivo. Mediante dos estudios de casos sobre una red de hackers desmantelada en Quebec en 2008 y la observacion del principal foro de debate de piratas informaticos durante 27 meses, entre 2009 y 2011, se ilustran en este articulo los retos concretos a los que se enfrentan los ciberdelincuentes para ganarse y mantener la confianza de sus companeros, que cuentan con numerosas razones para desertar sin riesgo de ser objeto de sanciones. En particular, se analiza el caracter fragil y efimero de las relaciones de confianza, asi como el papel que desempenan las normas culturales transgresivas que impiden a las comunidades de hackers aprovechar plenamente los beneficios de los instrumentos de gestion automatizada de la reputacion.
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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.004 |
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".