Well-Being Literacy in the Academic Landscape: Trioethnographic Inquiry Into Scholarly Writing
Bibliographic record
Abstract
Writing well and being well as academic writers is rarely spoken about, often hidden, and at times evaded. We believe that developing, maintaining, and growing well-being literacy not only engages the act but also allows awareness, reflection, and metacognitive thinking that enable mindful writing for well-being. Well-being literacy, the capacity to understand and employ well-being language for personal, collective, and global well-being, intrigues us. It encompasses nurturing, sustaining, and safeguarding well-being for individuals, groups, and systems to thrive. As scholars delving into well-being literacy, we, a diverse collective from across higher education career trajectories, investigate its role in scholarly writing and our academic realities. Our focus lies in unraveling the paradoxes inherent in higher education, particularly as researchers and writers. In this paper, we examine our own stories as a trioethnography and the impact of our writing practices on our own professional and personal lives. By doing so, we reveal the place of vulnerability, relationships, and meaning in who we are and are becoming as academic scholars. Guiding principles are shared with peers and colleagues in how they might cultivate writing practices while valuing and embodying well-being in the higher education space.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.023 | 0.049 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".