Bibliographic record
Abstract
Abstract The problems facing the world are complex and to develop successful solutions, the individuals working on these problems require knowledge across a variety of fields. Currently our educational system produces individuals who specialize in specific areas; however, the development of multi/interdisciplinary institutes and educational programs is a method that can be used to produce graduates with a broad range of expertise and problem-solving abilities. This work aims to evaluate the important factors for a successful multi/interdisciplinary initiative. For this work, a very broad definition of multi/interdisciplinary initiative was used, which included any higher education institute or academic program that was created to leverage collaboration across different disciplines. A number of different potential factors are investigated including examining their temporal importance. One-on-one semi-structured interviews were conducted with five directors of multi/interdisciplinary initiatives that are connected to universities in North America. Data is currently being analyzed using thematic analysis (Braun & Clarke, 2006), with insight from coding methods suggested by Saldaña 2021 (2021). Given the exploratory nature of this research, no theoretical framework was employed for data analysis. Preliminary results indicate the importance of synergy and ways of fostering increased synergy across the life cycle of a multidisciplinary institute. There are also indications of essential factors that must be in place as a foundation to support the initiation and growth of such initiatives. A temporal element in the factors for success was established regarding vital times to focus on knowledge connection and then move towards knowledge creation and knowledge mobilization. With an increased effort across academia and industry to develop multi/interdisciplinary initiatives to tackle the world's grand challenges, the results of this study can help directors plan for the creation and administration of new initiatives. Furthermore, it will help inform members of new and existing multi/interdisciplinary projects about the importance of synergy and collaboration and how it can be enhanced throughout the life cycle of an initiative. References: Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa Saldaña, J. (2021). The Coding Manual for Qualitative Researchers. United Kingdom: SAGE Publications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".