Developing the Tools to Manage Complex Crises
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With an increase in globalization and the rise of new and reemerging diseases, there is potential for widespread disease outbreaks and dissemination. Evidence shows individuals with an established appreciation for, and understanding of, an interdisciplinary framework for problem solving have an advantage in dealing with major global crises. The Integrated Training Program in Infectious Disease, Food Safety and Public Policy (ITraP) was recently developed at the University of Saskatchewan, Canada, to build these interdisciplinary skills in young professionals. This article presents the benefits and advantages of this type of training, by providing real-world examples of how knowledge and skills emphasized in ITraP teachings provide methods to assist in controlling epidemic situations. Moreover, to further the conversation about these training programs and to aid groups who are considering developing similar programs, this article discusses lessons learned from the first few years of ITraP’s inception, including the major barriers to success. We found that although interdisciplinary training programs are becoming increasingly necessary to deal with problems in our complex world, there are still a multitude of obstacles to be considered prior to the development and implementation of such a multifaceted program. Therefore, it is important that as these types of training programs begin to grow and evolve, researchers begin a dialogue regarding what types of teaching methods to employ, what interdisciplinary theories to use, and whether there is any evidence of success and sustainability.
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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 it