Population Ecology (Organizational Ecology): An Experiential Exercise Demonstrating How Organizations in an Industry Are Born, Change, and Die
Bibliographic record
Abstract
This article describes a classroom exercise that is designed to help students understand the basic tenets of population ecology (also known as organizational ecology). The macro-level, longitudinal approach to understanding organizations can be difficult for students to conceptualize as it involves systems thinking. This exercise makes the theory come alive by asking students to put themselves directly into the role of an organizational decision maker in an evolving industry. Over the course of one class, students get to experience how organizational size/age, environmental factors, and even random chance can affect organizational success and the makeup of an industry. Simulating up to a decade or more, students learn that populations of organizations change in predictable ways. We have tested this exercise with hundreds of students and we present evidence that it is effective in teaching the principles of population ecology (postexercise testing average of 92%) and also engaging and enjoyable for students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".