Artificial Intelligence and the Next Frontier of Organizational Modeling
Bibliographic record
Abstract
By suggesting how a computer can be programmed to reproduce complex human behaviors, artificial intelligence (AI) has afforded innovative approaches to develop formal models of organizations and advance theory in the literature on strategy and organizations. Recently, relevant and interesting innovations such as deep learning (artificial neural networks) have emerged from the field of AI and have provided new research opportunities for formal modeling. The purpose of this panel symposium is to explore such recent developments in this field and discuss how its algorithms and concepts can offer new interesting insights on organizational phenomena. To this aim, we have gathered together four leading scholars on organizational modeling and AI, who will provide a series of presentations to prime our panel discussion/audience Q&A session. Panel presentations will frame our session by reviewing the recent developments in AI and by sharing each other's work on how its algorithms and concepts can be integrated into organizational modeling. A subsequent panel discussion and audience Q&A will synthesize these presentations and push our collective thinking forward to inspire future research and directions on organizational modeling.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".