Bibliographic record
Abstract
Transitioning from graduate student to early career faculty can often provoke uncertainty and questioning. This study explores the rhetorical and revealing nature of such questioning (i.e., Am I really this lost? Am I in the right place?). Utilizing methods from arts based research (Barone & Eisner, 2012), specifically poetic inquiry (Prendergast et al., 2009; Richardson, 1992), we created found poetry around rhetorical questions from our existing collaborative autoethnographic journal. We frame our findings with a selection of poems to provide insight into our lived experiences of transition. The question poems illustrate that our first year as assistant professors were preoccupied with managing tasks, balancing work, avoiding burnout, building relationships, and discovering how to belong in the new context. While rhetorical questions do not necessarily produce answers, questioning in a collaborative space allowed us to explore the struggle, complexity, and ambiguity of academic identity construction as early career faculty.
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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 | 0.029 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".