Bibliographic record
Abstract
The notion of academic identity often refers to several different but related phenomena (Flowerdew & Wang, 2015; Gee, 2000), the first among which are the characteristics of being employed as a professor at a university and all the challenges that come with it as a professional workplace. For example, Smith et al. (2022) note that “academic identity has been a topic of interest for many years, often dealing with the complexity of the academic role within organisations where significant ‘professionalism’ (migration to key performance indicators and managerialism) has taken place following a period of significant expansion in student numbers and a move towards neoliberalism” (p. 1294). In this case the term academic identity is perhaps akin to the notion of professional identity as it shares much in common with other workplace identities and the relationships and power struggles that come with it (Billot, 2010; Clegg, 2008; see also Drennan et al., 2017; Henkel, 2005). Yang et al. (2022) discuss the role of emotional resilience in navigating this process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".