De-boning the fish: Indexing Routledge’s <i>Teaching Creative Writing in Asia</i> as abecedarian memoir
Bibliographic record
Abstract
This essay of creative nonfiction tells the story – one intellectual, professional, personal, and cultural – of the author having recently edited a ground-breaking anthology of Creative Writing pedagogy. While the Routledge anthology is formal scholarship, its editor and contributor here writes creative nonfiction to (a) more personally reflect on some of the massive cultural issues involved in the creation and direction of many of the first CW programmes in Asia (e.g. in a master’s degree conferred by Goldsmiths, University of London to students at Singapore’s LASALLE College of the Arts), through (b) a textual dialogue with some of the highlights from the Routledge anthology’s index. As a starting point, and one not shared in the 2021 anthology itself, this essay shares the ‘dirty little secret’ that while indices are, by definition, often one of the last components written for a book, a book’s internal cartography would be improved if an index could be written earlier. As both this essay and the anthology point out, creative nonfiction is a multiply apt genre for this inquiry, given its (i) popularity, (ii) easy transfer to social media sharing and (iii) greater licence, in anglophone publishing, to play with form.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".