Basisboek Cybercriminaliteit:Een criminologisch overzicht voor studie en praktijk
Bibliographic record
Abstract
Cybercriminaliteit is de afgelopen jaren, mede door de digitalisering van de samenleving, aan een flinke opmars bezig. Het thema cybercriminaliteit krijgt dan ook een steeds prominentere plaats in criminologisch onderwijs en onderzoek. Belangrijke vragen zijn: wat valt er precies onder cybercriminaliteit? Hoe ziet de criminaliteit eruit en in welke opzichten verschilt het van traditionele criminaliteit? Wie zijn de daders en slachtoffers? En wat zijn de implicaties voor de toepassing van criminologische theorieën en voor de aanpak? Dit studieboek geeft antwoord op deze vragen. Experts van verschillende universiteiten en onderzoeksinstellingen in Nederland brengen in dit boek op een overzichtelijke en toegankelijke manier nationale en internationale kennis samen over het thema cybercriminaliteit.
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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 | 0.021 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.026 | 0.026 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".