Using Duoethnography to Cultivate an Understanding of Professionalism: Developing Insights into Theory, Practice, and Self Through Interdisciplinary Conversations
Bibliographic record
Abstract
In this chapter, we examine what it means to be a professional by unpacking how professionalism is interpreted within nursing, counseling, and teaching contexts. Diversity, regarding the notion of professionalism, yields important information about how the concept is understood by individuals. Our dialogic and dialectic encounters facilitated deeper reflection on our own personal biases and understandings, which in turn shifted our beliefs about how professionalism is enacted within the workplace. This transformation in our way of thinking helped us to better articulate for others, and ourselves, what it means to be a professional. Together, we have come to understand that professionalism is not a concrete and static concept, but rather a dynamic web of interconnectedness that guides our growth and development as individuals. This work contributes to the duoethnography literature by illustrating how it can be used as an interdisciplinary approach for promoting professional reflective practice. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".