Understanding and expressing academic identity through systematic autoethnography
Bibliographic record
Abstract
This article describes an autoethnographic approach that can be used by academics in higher education to better understand how their academic identity is constructed. The article emphasizes considerations for academics with non-traditional roles that are not discipline-focused, while also providing relevant methods for academics with any role who wish to explore their academic identity at its core. A detailed systematic approach to autoethnography is outlined, with data collection, data analysis, and methods of creating expressions of academic identity described. This approach to autoethnography provided new understandings regarding how the author’s academic identity has been constructed over time in ways that extended and deepened insights from reviewing the literature and writing unstructured reflections alone. The creation of expressions of academic identity from this study represent valuable professional development outcomes, which can also help to bring professional goals into better focus. Overall, this article demonstrates that the use of autoethnography to explore academic identity can not only lend clarity and depth to one’s conceptualization of this, but can also transform the perception of oneself as an academic as a result.
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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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".