Establishing a transdisciplinary research team in academia.
Bibliographic record
Abstract
The establishment of a transdisciplinary research team, the Applied Developmental Neuroscience group, is described. The group, which initially included a physical therapist, occupational therapist, speech-language pathologist, and developmental pediatrician, is focused on linking theory and practice for intervention with children with disabilities. The group encountered challenges related to personal attributes such as trust, communication, time, and commitment; the task of finding a common theoretical perspective; and the autonomous academic environment. The strategies used to address these challenges and the benefits of a transdisciplinary model, such as cross-fertilization of theoretical perspectives and more powerful application and testing of theoretical frameworks in a research context, are described.
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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 | 0.075 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".