Combating Child Pornography: Steps Are Needed to Ensure That Tips to Law Enforcement Are Useful and Forensic Examinations Are Cost Effective
Bibliographic record
Abstract
A letter report issued by the Government Accountability Office with an abstract that begins "The Department of Justice (DOJ) reports that online child pornography crime has increased. DOJ funds the National Center for Missing and Exploited Children (NCMEC), which maintains the CyberTipline to receive child pornography tips. The Providing Resources, Officers, and Technology To Eradicate Cyber Threats to Our Children Act of 2008 (the Act) contains provisions to facilitate these investigations and create a national strategy to prevent, among other things, child pornography. The Act directed GAO to report on actions to minimize duplication and enhance federal expenditures to address this crime. This report examines (1) the extent to which NCMEC determines the usefulness of tips; (2) mechanisms to help law enforcement coordination (i.e., deconfliction); and (3) the extent to which agencies are addressing factors that federal law enforcement reports may inhibit investigations. GAO analyzed the Act and spoke to law enforcement officials who investigate these crimes, selected to reflect geographic range, among other things. Although these interviews cannot be generalized, they provided insight into investigations"
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".