Digital Paratext, Editorialization, and the Very Death of the Author
Bibliographic record
Abstract
As shown by different scholars, the idea of “author” is not absolute or necessary. On the contrary, it came to life as an answer to the very practical needs of an emerging print technology in search of an economic model of its own. In this context, and according to the criticism of the notion of “author” made during the 1960–70s (in particular by Barthes and Foucault), it would only be natural to consider the idea of the author being dead as a global claim accepted by all scholars. Yet this is not the case, because, as Rose suggests, the idea of “author” and the derived notion of copyright are still too important in our culture to be abandoned. But why such an attachment to the idea of “author”? The hypothesis on which this chapter is based is that the theory of the death of the author—developed in texts such as What is an Author? by Michel Foucault and The Death of the Author by Roland Barthes—did not provide the conditions for a shift towards a world without authors because of its inherent lack of concrete editorial practices different from the existing ones. In recent years, the birth and diffusion of the Web have allowed the concrete development of a different way of interpreting the authorial function, thanks to new editorial practices—which will be named “editorialization devices” in this chapter. Thus, what was inconceivable for Rose in 1993 is possible today because of the emergence of digital technology—and in particular, the Web.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".