Bibliographic record
Abstract
Citation (2012), "Advisory Board", Lomi, A. and Harrison, J.R. (Ed.) The Garbage Can Model of Organizational Choice: Looking Forward at Forty (Research in the Sociology of Organizations, Vol. 36), Emerald Group Publishing Limited, Leeds, p. xi. https://doi.org/10.1108/S0733-558X(2012)0000036003 Publisher: Emerald Group Publishing Limited Copyright © 2012, Emerald Group Publishing Limited Book Chapters The Garbage Can Model of Organizational Choice: Looking Forward at Forty Research in the Sociology of Organizations Research in the Sociology of Organizations Copyright Page List of Contributors Advisory Board The Garbage Can Model of Organizational Choice: Looking Forward at Forty “A Garbage Can Model” At Forty: A Solution that Still Attracts Problems Turn-Taking and Geopolitics in the Making of Decisions Mechanisms Generating Context-Dependent Choices Chance, Preferences, and Predictions in Garbage Can Theory Garbage Can Ecologies: An Agent-Based Exploration Aspects of Garbage Can Processes: Temporal Order and the Role of Expediting Garbage Can in the Lab Team Formation in the Garbage Can Modeling Formal and Informal Ties Within an Organization: A Multiple Model Integration Situated Attention, Loose and Tight Coupling, and the Garbage Can Model Structure, Skill, and Ambition in Organizational Problem Solving From the Ivy Tower to the C-suite: Garbage Can Processes and Corporate Strategic Decision Making Organized Anarchies and the Network Dynamics of Decision Opportunities in an Open Source Software Project Organizational Decision Mechanisms in an Architectural Competition Geopolitics and Garbage Cans: Understanding the Essence of Decision Making in an Interdisciplinary and Psycho-Cultural Perspective
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.011 | 0.005 |
| Insufficient payload (model declined to judge) | 0.694 | 0.632 |
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".