Censorship is futile possible but difficult: A study in algorithmic ethnography
Bibliographic record
Abstract
Discourse around censorship tends to be sensationalised in many quarters. Nabi (2014), for example, recently sought to “prove ... the futility” of governments engaged in censorship programmes through the Streisand Effect (Greenberg, 2007). While most countries have an imperfect censorship regime, the sovereign rights of nations to make their own laws must be recognised, including (but not limited to) the protection of children, and the victims of child exploitation, gambling addicts, and Internet banking users, whose systems may be infected by malicious software, resulting in financial losses. The broader question to be posed seems to be, under what circumstances is censorship justified, and how can it best be achieved? In this paper, we present the results of a study that illustrates the overwhelming harms to users that emerge from an unregulated Internet regime: 89 percent of ads delivered to Canadian users on 5,000 rogue sites for the most complained-about movies and TV shows were classified as “high risk”. We conclude that more granular policies on what should be censored and better tools to enforce those policies are needed, rather than accepting that censorship is impossible.
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 | 0.027 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.029 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".