Bibliographic record
Abstract
Purpose This paper aims to respond to Yammarino's article in this issue on level of analysis and the US Constitution. Design/methodology/approach This paper expands on two concepts central to levels of analysis: entity and causal process. Then additional alternative ways of conceptualizing, analyzing, and representing multi‐level organizations – beyond the organization chart – are described. A rationale for America's use of the Electoral College is sought. Findings The paper reveals connections among traditional notions of hierarchy (including the traditional organization chart) and contemporary social network concepts. Practical implications Leaders and other members of social and organizational systems should be mindful of their mental representations of hierarchy, of organizational or social groupings (e.g., US States), and of social distance. These representations can influence behaviors and perceptions, including perceived fairness of procedures. Originality/value The paper presents interesting information on connections among traditional notions of hierarchy and contemporary social network concepts.
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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 | 0.048 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.027 | 0.025 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".