Embracing Complexity and Systems to Bridge the Corporate-Society Divide
Bibliographic record
Abstract
The purpose of this symposium is to generate a discussion across business sub-disciplinary fields on how to embrace complexity and systems approaches in management research. This involves understanding the impacts of business on societal and environmental systems, but also how businesses are actors, leaders and decision-makers within complex systems. Whilst there is a strong rationale for WHY this kind of research is needed, the HOW question is still relatively underdeveloped. The panel consists of leading scholars, from a range of sub-disciplines, who are addressing the HOW question. The panel discussion will explore emerging theoretical, methodological, and empirical approaches to understanding the interaction between business and system-level outcomes, and the challenges of doing so. By creating a space for discussion between sub-disciplinary boundaries, we hope to develop new research directions on how complexity theory and systems- oriented approaches can enhance our understanding of business within societal systems.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".