Bibliographic record
Abstract
In Edited Clean Version, Guins argues that media censorship is not restricted to the actions of external institutional forces that impose their censorial regulations from above.Rather, censorship can also be found when individuals use 'technologies of control' in the privacy of their homes.Discussing a variety of technologies that allow individuals to filter the media exposure of family members -such as the television V-Chip, filtering DVD players, "clean" version CDs, video game patches, and Internet filtering software -Guins points to a larger cultural shift in our censorship practices.In our era of digital technology, censorship has become an increasingly self-initiated process, and individuals purchase filtering technologies for personal and familial use so that they may self-censor the information to which they are exposed.Within this framework the very act of censorship becomes reframed as a question of user choice and individual freedom, as a feature that we intentionally choose in order to filter and block 'harmful' media content.Thus the notion of censorship is being remade, and rather than being presented as an oppressive force imposed from above it is now marketed as a technological solution that is empowering, liberating, and enabling.Guins questions this uncritical celebratory stance, arguing that because these technologies control an individual's access to information (however willingly they may be used), they should still be understood within a framework of governance.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.240 | 0.165 |
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".