Bibliographic record
Abstract
This book offers a genealogy and theory of the improper name—defined as the adoption of the same pseudonym by organized collectives, affinity groups and individual authors. Improper Names focuses on shared pseudonyms that make their appearance in Europe and North America at three critical historic junctures: the Industrial Revolution, the shift from Fordism to post-Fordism, and the emergence of the network society. While proper names designate a referent as part of the operation of a system of signs, improper names fail to circumscribe a clearly defined domain. An improper name differs from other collective subjects of enunciation in that its progenitors are ultimately unable to control third-party usages. The book examines this tension between centralization and decentralization, the We and the I, by focusing on five case studies: Ned Ludd, a pseudonym shared by the English machine breakers of the early nineteenth-century known as the Luddites; Alan/Allen Smithee, an alias adopted by Hollywood film directors from 1969 to 1999 to disown films that were re-cut by film producers; Monty Cantsin, an “open pop star” created by U.S. and Canadian mail artists in the late 1970s to share their reputation and critique bourgeois notions of authorship as originality and novelty; Luther Blissett, a folk hero of the information age introduced by Italian media activists in the 1990s to prank the media and the culture industry; and Anonymous, a signature globally shared by Internet activists to protest against censorship and restricted access to information and information technologies.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.094 | 0.043 |
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".