Sharing Tacit Knowledge of Academic Publishing: How to Respond to Reviewer Comments
Bibliographic record
Abstract
Abstract: Navigating academic publishing is trying and complicated for both junior and established scholars. Some of the demands from the process come from the hidden curriculum within academic culture which is exacerbated by the tacit knowledge that only some folks possess. In this editorial, I explain some of my personal struggles with academic publishing as a First in the Family (FiF)/First Generation Student (FGS). I utilize my understanding as a way to frame an issue within academic publishing that I have personally experienced and observed in my CJNSE editorship: responding to reviewer comments. I then outline the components academic authors must include when replying to their peers’ assessments of their manuscripts, and provide an example of a method of how to do so. Fittingly, the work of the authors, Review Mentors (graduate student peer mentors), and Senior Review Editors (PhDs with publishing experience) in this issue highlights the mentoring qualities of CJNSE and how tacit knowledge can be shared in this publishing environment. The topics discussed in this issue include the shortcomings within English language learning courses for immigrants and refugees to Canada (Lam); the different qualities and emotional intelligence required of department chairs (Cowley); the need for curricula change to include death, dying, and grief in elementary curricula (Durant); querying the principalship as a democratic process (Kendrick); questioning the idea of universal values for education policy educators (Hankey); the tensions between Deweyan and Confucian educational philosophies in English language learning in China (Peng); the factors that mediate a language teacher's corrective feedback decision (Chen); the factors that affect an individual’s likelihood of reporting sexual assault on a post-secondary campus (MacKenzie); the ways in which washback and curriculum agreement are interconnected methods of classroom instruction (Sultana); the limitations and utility of Kimberly Maich & Carmen Hall’s “Autism Spectrum Disorder in the Ontario Context: An Introduction,” and explaining the importance of Jen Gilbert’s “Sexuality in School: The Limits of Education” to educators, particularly those in Canada (Virani-Murji).
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 | 0.182 | 0.737 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.024 | 0.016 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".