Online Child Sexual Exploitation: A New MIS Challenge
Bibliographic record
Abstract
This paper deals with the difficult yet increasingly important MIS phenomenon of online child sexual exploitation (online CSE). Through the use of secondary and publicly available data from the Federal Bureau of Investigation, as well as primary data from a cybercrime police unit in the United Kingdom, this study takes a grounded theory approach and organizes the role that technologies and social actors play in shaping online CSE. The paper contributes to IS theory by providing a consolidated model for online CSE, which we call the technology and imagery dimensions model. This model combines the staging of the phenomenon and the key dimensions that depict how the use of technology and imagery both fuels and defuses the phenomenon. In informing the construction of the model, the paper extracts, organizes, and generalizes the affordances of technology and discusses the role of information systems in detecting online CSE.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.014 | 0.038 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".