Bibliographic record
Abstract
As an academic writing coach and developmental editor, I have worked with scores of humanists and social scientists on successfully revising their dissertations into books. A first step in revision is understanding the distinctiveness of the academic monograph as a genre, particularly its requirements in terms of scope, voice, and through-line. In this article, I describe the common stages of reconceptualizing the project and revising the text, as well as the strategies I have found effective in helping authors move through the stages as adeptly and efficiently as possible. Drafting a book proposal is a challenging but often key step. Later stages include incorporating new research, revising and expanding some chapters and possibly cutting one or more, and soliciting feedback on the new articulations of ideas from colleagues. At every stage of this iterative process, authors need to have patience and compassion for themselves.
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 | 0.071 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.025 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 0.011 |
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".