Collective Intelligence in Organizations and Organizing
Bibliographic record
Abstract
This symposium presents cutting edge research on collective intelligence (CI). CI is the phenomenon of groups outperforming even the most skilled individuals. Organizations are a key way through which societies constitute groups and structure their interactions and therefore is a seat of collective intelligence. The papers here address some of the ways group processes are structured and the implications for organization performance. Behavior Moderates the Effect of Network Structure in Collaborative Problem Solving Author: Christoph Riedl; Northeastern U. Author: Zachary Fulker; Northeastern U., Boston Massachusetts Author: Julian Gullet; Northeastern U. Metawisdom of the Crowd: When Choice Within Aided Decision Making Can Make Crowd Wisdom Robust Author: Jon Atwell; Stanford Graduate School of Business Author: Marlon DeMarcie Twyman; U. of Southern California, Annenberg School for Communication and Journalism A Hidden Dimension of Human Capital in Collective Intelligent Systems Author: Hyejin Youn; Northwestern Kellogg School of Management Author: Frank Neffke; Complexity Science Hub Vienna Author: Letian Zhang; Harvard Business School Author: Seyed Mohamad Hosseinioun; Kellogg School of Management, Northwestern U. Beliefs are Not Decisions: How Wise Crowds Make Unwise Choices Author: Joshua Becker; UCL School of Management Author: Jingze Wang; UCL School of Management
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".