The Politics of Legal Challenges to Pornography: Canada, Sweden, and the United States
Bibliographic record
Abstract
The dissertation analyzes obstacles and potential in democracies, specifically Canada, Sweden, and United States, to effectively address empirically documented harms of pornography. Legislative and judicial challenges under different democratic and legal frameworks are compared. Adopting a problem-driven theoretical approach, the reality of pornography’s harms is analyzed. Evidence shows its production exploits existing inequalities among persons typically drawn from other forms of prostitution who suffer multiple disadvantages, such as extreme poverty, childhood sexual abuse, and race and gender discrimination, making survival alternatives remote. Consumption is also divided by sex. A majority of young adult men consumes pornography frequently; women rarely do, usually not unless initiated by others. After consumption, studies show many normal men become substantially more sexually aggressive and increasingly trivialize and support violence against women. Vulnerable populations—including battered, raped, or prostituted women—are most harmed as a result. The impact of attempts to address pornography’s harms on democratic rights and freedoms, specifically gender equality and speech, is explored through the case studies. Democracies are found to provide more favorable conditions for legal challenges to pornography’s harms when recognizing substantive (not formal) equality in law, and when promoting representation of perspectives and interests of groups particularly injured by pornography. State-implemented approaches such as criminal obscenity laws are found less effective. More victim-centered and survivor-initiated civil rights approaches would be more responsive and remedial—a finding with implications for other politico-legal problems, such as global warming, that disproportionately affect disadvantaged populations traditionally largely excluded from decision-making.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.013 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".