Improving Doctoral Educator Development: A Scaffolding Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In response to a need for improved training of business school teaching, this research explores US doctoral programs in management and finds a need to purposefully embed scaffolding—the process of gradually enabling the doctoral student to take on more challenging aspects of teaching—into doctoral program design. We also recommend a more influential role to be played by professional organizations to address doctoral educator development. As we followed a grounded theory approach, our methodology started with an analysis of program marketing documents and materials followed by behavioral event interviews (BEIs) and perceptual interviews (PIs) with doctoral students in management. Following coding, we reviewed the literature on doctoral education to explore how our emergent data mapped against prior research. By also taking into consideration the lived experience of students, the study data provides evidence that doctoral programs are not properly designed to support educator development. We discuss our findings related to what programs do to support students and what students do to support themselves. Theorizing from our data, we present our model that illustrates how programs could embed scaffolding to support programs’ commitment to develop future educators.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it