Examining EdD Dissertations in Practice: The Carnegie Project on the Education Doctorate
Bibliographic record
Abstract
In 2007, 25 colleges and schools of education (Phase I) came together under the aegis of the Carnegie Project on the Education Doctorate (CPED) to transform doctoral education for education practitioners. A challenging aspect of the reform of the educational doctorate is the role and design of the dissertation or Dissertation in Practice. In response to consortium concerns, members of the CPED Dissertation in Practice Awards Committee conducted this action research study to examine the format and design of Dissertations in Practice submitted by (re) designed programs. Data were gathered with an online survey, interviews, analyses of 25 Dissertations in Practice submitted in 2013 to the Committee. Results indicated few changes occurred in the final product, despite evidence of change in the Dissertation in Practice process. Findings contribute to debates about the distinctive nature of EdDs (and of professional doctorates generally) as distinct from PhDs, and how about the key criteria for demonstrating “new knowledge to solve significant problems of practice” are demonstrated through the dissertation submission.
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 | 0.225 | 0.244 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.031 | 0.024 |
| Scholarly communication | 0.021 | 0.010 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".