Navigating Teacher Professionalism in Autoethnography: Ethical and Occupational Dilemmas in Professional Learning Reflections
Bibliographic record
Abstract
Ongoing professional learning and engaging in practitioner-based research are characteristic qualities of teachers’ transformative professionalism. Autoethnography may align with the objectives of promoting autonomous adult learning through systematic reflection to enhance professional practice. Through an autoethnographic method, I generate a vignette disseminating my experiences as a public school teacher engaging in autoethnography as a reflective professional learning strategy to bolster my pedagogical/collegial practices. Findings generated via my reflective inquiry demonstrated that autoethnography may advance teachers’ transformative professionalism through autonomous exercise of knowledge and self-directed learning, and may bridge the gaps between theory and praxis in educational contexts. However, my vignette further revealed the confining tensions in which organizational professionalism ideology may simultaneously control and impede the scope, content, and dissemination of teachers’ autoethnographies, resulting in subtle forms of self-censorship. Further, this study generates new methodological insights into the procedural construction of a teacher’s autoethnography, specifically revealing the ethical challenges I navigated as a teacher engaging in autoethnography, a process that necessitated difficult decision-making regarding my own willingness to be occupationally vulnerable and measures to safeguard students’ confidentiality. Consequent to this inquiry, I generated practical recommendations to guide teachers interested in engaging autoethnography as a learning reflection tool and outline considerations to ensure authors’ physical, emotional, and occupational safety.
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 | 0.062 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.030 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".