Literature at lightspeed: a community of writers on the world wide web and its relationship to the print publishing industry
Bibliographic record
Abstract
The World Wide Web offers individual writers new possibilities for producing and distributing fiction. This dissertation begins with an in-depth look at who these writers are, what they are doing and the advantages and disadvantages the Web has compared to traditional publishing venues. The common goals of these writers and the various ties which bind them suggests that a community of writers has developed on the Web. No community exists in isolation, however, so the dissertation also looks at some of the other forces at work in society which may have an effect on this community. Transnational entertainment conglomerates, for instance, are attempting to change the underlying structure of the technology in order to reap potentially great profits from it; their efforts may result in diminishing the ability of individual writers to use the Web to effectively distribute their work. Governments, to use another example, can affect the way individuals use the medium by enacting laws restricting certain categories of content online or developing copyright laws which favour the interests of large entertainment producing corporations. Finally, writers publishing on the Web may have a disintermediating effect on traditional print publishing, with ramifications for, among others, publishing houses and bookstores. What emerges in this dissertation is a portrait of the complex web of relationships between individual and institutional stakeholders in this developing technology.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.018 | 0.016 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".