Toward a Common Structure in Demographic Educational Modeling and Simulation: A Complex Systems Approach
Bibliographic record
Abstract
This article identifies elements and connections that seem to be relevant to explain persistent aggregate behavioral patterns in educational systems when using complex dynamical systems modeling and simulation approaches. Several studies have shown what factors are at play in educational fields, but confusion still remains about the underlying mechanisms driving observed outcomes and therefore more guidance is needed. The framework suggested here throws some ideas in that direction stressing the relevance of nonlinear complex interactions via feedbacks between education systems’ transition rates ─ intake, repetition, dropout, and promotion ─ and schooling outcomes. Schooling outcomes reciprocally influence transition rates in the system generating aggregate patterns that continuously change (and are changed by) the inputs that endogenously determine them. Furthermore, this paper underscores practical and theoretical limitations of traditional quantitative models that can be addressed with a complex systems analysis and suggests future lines of investigation. Specifically, this article advocates a complexity approach ruled by the laws of thermodynamics to help detect corrupt practices in education systems and improve accountability and governance in such systems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".