ASAS-NANP SYMPOSIUM: Review of systems thinking concepts and their potential value in animal science research
Bibliographic record
Abstract
Worldwide, our collective research and policy institutions, including the American Society of Animal Science (ASAS), are calling for more systems-based research and analysis of society's most pressing and complex problems. However, the use of systems analysis within animal science remains limited and researchers may not have the tools to answer this call. This review thus introduces important concepts in systems thinking methodology, such as policy resistance, feedback processes, and dynamic complexity. An overall rationale for systems thinking and analysis is presented, along with examples of the application of these concepts in current animal science research. In order to contrast systems approaches to more frequently employed event-oriented research frameworks, both frameworks are then applied to the ASAS' identified "Grand Challenge" problem of antimicrobial resistance (AMR) in order to compare these two kinds of analyses. Systems thinking stresses the importance of underlying system structures that lead to persistent problem behaviors vs a focus on unidirectional cause-and-effect relationships. A potential systems framework for animal production decisions to use antimicrobials is shown that more explicitly accounts for AMR in a way that can lead to different animal production decisions than the event-oriented framework. Acknowledging and accounting for fundamental system structures that can explain persistent AMR will lead to different potential solutions to this problem than would be suggested from more linear approaches. The challenges and benefits of incorporating systems methods into animal science research are then discussed.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".