Evaluating the Effectiveness of Dissertation Boot Camp Delivery Models
Bibliographic record
Abstract
Dissertation boot camp (DBC) programs have been adopted at many postsecondary institutions across North America over the last decade.Responding to Simpson's (2013) call for writing centers to do more than simply share anecdotal information about the effects of their DBC programs, the authors of this mixed-methods study assess the benefits of these programs for doctoral students.The study evaluates three DBC delivery models-online, sustained, and retreat-in order to determine each model's effect on doctoral students' writing behaviors, confidence levels, and anxiety.By conducting a more robust statistical analysis than has been possible in other preliminary work on DBC programming, the paper corroborates Busl, Donnelly, & Capdevielle's (2015) finding that "Writing Process" DBCs are more beneficial to doctoral students than "Just Write" DBCs.The authors ultimately find that doctoral students experience positive outcomes from all three DBC models and are likely to self-select based on the model that best suits their individual needs.The results of this study indicate that postsecondary institutions ought to consider offering a variety of DBC programming in order to meet the needs of diverse graduate-student populations.
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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 | 0.054 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".