Facilitating collaborative interdisciplinary research: exploring process and implications for leisure scholars
Bibliographic record
Abstract
Many institutions encourage interdisciplinary research (IDR) to maximize organizational resources and to develop more practical approaches to address transdisciplinary ‘real world’ issues such as obesity. Leisure researchers have joined fields such as public health and kinesiology to address increased rates of obesity and physical inactivity with the perspective that these issues require integrated cross-disciplinary knowledge. This case study examined the collaborative process throughout an IDR project involving leisure, health education, physical education and STEM education faculty (five members). The major research questions addressed are as follows: (1) What are the synergies, opportunities and/or obstacles identified by faculty throughout the development, implementation and evaluation of the IDR program? (2) What are the implications of these findings for leisure researchers working in IDR? Faculty responded to email questions and surveys and were interviewed individually before and during the planning phase, during the implementation phase and at the end of the study. All data were transcribed and analyzed inductively, relying on the constant comparative method, with triangulation within and across different data types and member checks. Themes relating to synergies, constraints, understanding of collaborative processes and interdisciplinary knowledge were identified.
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 | 0.133 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.031 | 0.041 |
| Scholarly communication | 0.026 | 0.020 |
| Open science | 0.006 | 0.041 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".