Can It Clean Up Your Inbox? Evidence from South Korean Anti‐spam Legislation
Bibliographic record
Abstract
Although spam email messages have been the primary source of cybercrime since the early Internet era, there is no quick fix to this problem. Governments have established anti‐spam legislation, but surprisingly, there has been no measurement of policy impact. This study aims to fill the gap by utilizing a quasi‐experimental setting in South Korea, where the anti‐spam policy was substantially amended in November 2014. A significant change was in the default setting, switching from an opt‐out to an opt‐in scheme, which required that commercial email senders obtain recipients' prior consent. Also, the law notably escalated the deterrent penalties for perpetrators. To empirically examine the policy effectiveness, we use a large‐scale data set of 5.61 billion spam emails originating from over 38,000 spammers in 226 countries during twenty months in 2014–2015. Our findings suggest that the amended policy adopting the opt‐in scheme decreased the volume of spam originating from Korea by 16.1%. The expected economic gain from the increased productivity of recipients is 7.649 million USD per year. This study contributes to the literature by highlighting that a well‐designed policy can lower cybersecurity incidents that threaten organizations, operations, and individuals. Our finding also provides important implications for policymakers and managers in designing effective policies with data‐driven evidence.
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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 | 0.019 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".