Bibliographic record
Abstract
ConclusionCensorship stories no longer dominate the mainstream media the way they once did during much of the 1980s and early 1990s.Artists do not grapple with the same roadblocks they did thirty years ago, as media culture itself has shifted.When the National Gallery of Canada mounted the exhibition Pop Life: Art in a Material World in the summer of 2010, due to controversial subject matter in some photography and film, two out of eleven rooms were closed to audiences under the age of eighteen.1 A user comment on the Cape Breton Post website reads, "Someone under 18 ... would probably Google it, just like I just did, to see the lot.Censorship -tsk tsk!" 2 There was no Google during the censor wars.Film canisters had to be carted to and from the censor board's office, censored scenes were literally cut out of film prints, and home video was a burgeoning form.Regulatory censorship had real impacts in impeding the circulation of expression.So when overt kinds of censorship do happen in today's shifted mediasphere, they more obviously betray the failures of the protocols of dissuasion.The restricted rooms at Pop Life contained a variety of images of sexuality, meaning there are still battle lines drawn around expressions of sexuality, as well as around what is art and not art -or at least, not art for those under eighteen years of age. 3 Since the images at the National Gallery were readily available online and since the restrictions there (as well as at the Tate in London, from which the exhibition had travelled) were widely dis-
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.307 | 0.128 |
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".