Making the Time Fit the Crime: Clearly Defining Online Harassment Crimes and Providing Incentives for Investigating Online Threats in the Digital Age
Bibliographic record
Abstract
This Note examines online harassment and online stalking throughout the world, including the current landscape of Internet communication, the effects of cyberharassment and cyberstalking on its victims, and both the difficulties in defining these crimes in criminal codes and the difficulties in inspiring law enforcement to investigate complex internet crimes. Specifically, this Note discusses the problems inherent in current cyberharassment and cyberstalking treaties and legislation within the United States, Canada, and Australia. For example, this Note analyzes how these jurisdictions define cyberharassment and cyberstalking, how these definitions are inadequate for dealing with current forms of cyberharassment and cyberstalking (both due to inconsistencies between the definitions, as well as inherent roadblocks in proving the crimes as defined), and how the seriousness of these crimes as defined discourage law enforcement from using extensive resources to investigate these crimes. This Note then proposes a Model Statute that would amend existing U.S. federal law to consolidate definitions of cyberharassment and cyberstalking, to address existing difficulties in proving cyberharassment and cyberstalking crimes, and to address the ambivalence by law enforcement to investigate instances of cyberharassment and cyberstalking. These amendments would both empower citizens to better understand what conduct constitutes cyberharassment or cyberstalking, to more easily prove when cyberharassment and cyberstalking have or have not occurred, and to better empower law enforcement to delve into complex online investigations for cyberharassment and cyberstalking crimes.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".