Titles, experience, identities, and time: How the early career stage is defined by educational developers
Bibliographic record
Abstract
This article reports perceptions of what constitutes the early career stage by newer educational developers in contrast with those experienced in the field. We collected participants’ thoughts about what constitutes early career for educational developers using an online survey, and qualitative responses were analyzed using thematic analysis (per Braun & Clarke, 2006). Aligning with Super’s (1990) lifespan career development model, this exploratory research suggests there is no single, universal definition of what it means to be an early career professional in this field, and our research suggests that self-concept is crucial in determining means to be an early career professional in this field. Further, there is a distinction between being new to the field of educational development and being ‘early career’ in a broader sense; the research findings suggest that those who come to educational development as a second or third career understand the notion of ‘early career’ differently than those for whom educational development is a first career. We discuss key themes that emerged around how participants constituted the notion of ‘early career’ and offer some common vocabulary to identify and discuss experiences of early career professionals in educational development. This research may provide new opportunities for supporting onboarding and community building, and raises areas for further exploration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".