Research on Teaching in Physical Education Doctoral Dissertations: A Detailed Investigation of Focus, Method, and Analysis
Bibliographic record
Abstract
As a part of their doctoral education, students complete a dissertation. Examining these dissertations can provide one analysis of research in a field. The primary purpose of this study was to analyze all physical education dissertations with a teaching focus that were completed between 1985 and 1999. All possible dissertations were examined through the electronic version of Dissertation Abstracts International. For the teaching dissertations (n = 201), each abstract was coded for (a) research type, (b) research focus, (c) student variable measured, (d) observation used, (e) interview used, (f) other methods used, (g) population, (h) general methodology, and (i) statistics reported/used. Most research on teaching dissertations addressed issues related to teacher effectiveness and focused on motor skill learning and attitude. There was an increase in qualitative methods from those reported in a previous study (Silverman, 1987). While there were methodological advances, many dissertations still used methods that were not informed by the research methods literature.
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 | 0.081 | 0.193 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".