Review of "Detox Your Writing" and "Getting Published in Academic Journals"
Bibliographic record
Abstract
In an era of increasing pressure to publish and complete doctoral degrees as quickly as possible, all while managing heavy administrative workloads, it likely comes as no surprise that do-it-yourself (DIY) doctoral supervision tools are becoming increasingly prolific (Kamler & Thomson, 2008). Perhaps these materials are a response to a growing friction between time needed and time available for doctoral supervision, as well as between the crucial place writing occupies in a doctoral researcher’s life and the often tacit nature of apprenticing to become an academic. As both a doctoral student and a writing coach that works with other doctoral students, I am keenly interested in resources that can support me in both roles. Recently I picked up two texts to aid me with navigating my first attempts at publishing an article and with facilitating a doctoral writing workshop: Thomson and Kamler (2016), Detox your writing, and Paltridge and Starfield’s (2016), Getting published in academic journals. Both texts are geared toward a doctoral audience, though master’s level students and supervisors may equally appreciate the texts for their practical strategies. The texts are complementary as well. Where Thomson and Kamler focus mostly on the journey toward producing a dissertation, Paltridge and Starfield pick up from there to discuss how and why to consider publishing articles from the dissertation.
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 | 0.033 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| 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".