It “Made Me Who I Am”: Using Interpretive and Narrative Research to Develop a Model for Understanding Associate Deans’ Application and Development of Academic Identity
Bibliographic record
Abstract
Interpretive and narrative research approaches, the experiences of academic administrators other than deans, chairs, and presi-dents, and academic identity work beyond graduate students and beginning professors are all areas that are underrepresented in the literature on higher educational administration. This article builds on recent narrative research by applying higher educational admin-istrative theories as interpretive lenses to propose a model for helping to understand the development and application of associate deans’ academic identities. Among the findings were that academic identities helped explain associate deans’ approaches to their roles, their views of their surrounding organizations, and their reasons for assuming the role. Further, the associate deans who partic-ipated in this research did not experience their transition to the role as an identity crisis in the ways typically described and assumed by higher educational leadership scholars. Instead, they drew upon their well-established academic identities and, in keeping with the nascent research on academic identity work, were intentional in their efforts to maintain their academic identities.
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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 | 0.030 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.047 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".