Bibliographic record
Abstract
Many accounts that describe the procedures of academic writing focus on how authors can attract publishers by revising their dissertations so that they have appeal beyond their more narrow academic audiences. Few of these accounts, however, consider what happens when that process succeeds—that is, what happens to a manuscript after a publisher accepts it. This essay follows up on my 2011 JSP article, ‘What I've Learned about Revising a Dissertation,’ by considering those issues that arise during the production process of academic publishing. These stages are crucial for the success of a book, and they are avowedly collaborative in ways that differ from revising a dissertation. This process is often perceived as mere manufacturing when in fact it necessitates answering crucial conceptual questions. Furthermore, the customs and conventions of publishing are not a typical part of most academic training. In this essay, I draw from my own experience of publishing a title with an academic press to offer practical as well as theoretical reflections on how to select a publisher, write a book proposal, submit a manuscript, respond to readers' reports, think about copy-editing and proofreading, design a book jacket, and market a book after its physical publication.
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 | 0.019 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.041 |
| Scholarly communication | 0.027 | 0.043 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".