Strategies for Publishing in the Humanities: A Senior Professor Advises Junior Scholars
Bibliographic record
Abstract
This essay provides an introduction to publishing in the humanities for junior scholars at the start of their careers and beyond. It reflects on how our relationships to publishing change along our career paths. Indeed, while the focus on publications is significant during our junior years and continues after tenure, the pressure feels more intense and the possibility of publishing for promotion to full professor seems more elusive at the same time. The author confesses her own struggle in writing a second book. Transcending that hurdle—from associate to full—is, arguably, the most difficult challenge for most academics, particularly for women of colour, whose numbers remain abysmal at the rank of full professor. The essay provides a number of insights and strategies, taken from the author's experiences, discussions with other academics, and research, for successful publishing and promotion in the academy.
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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 | 0.042 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.038 | 0.017 |
| Scholarly communication | 0.034 | 0.022 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 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".