Bibliographic record
Abstract
Abstract Theorists and practitioners have debated the nature of educational systems and the most appropriate conceptualization of educational problems. Although terminology is idiosyncratic, both hard and soft systems thinking (SST) are evident in educational discourse. However, given that the school system has precise required outcomes (i.e. student achievement) coupled with subjective interpretation of those requirements (i.e. definition of an educated person), defining educational thought as either a hard or soft seems inappropriate and counter‐productive. Based on the assumption that human activity includes equally consequential objective and subjective realities, firms systems thinking is proposed as a unifying paradigm of educational problem solving. Firm systems thinking (FST) begins with the assumption that elements in a system are interconnected and interdependent. FST is appropriately applied to systems that: (1) have objective elements that are subject to individual interpretation; (2) have both precise and imprecise requirements and specifications and (3) focus on both micro (i.e. specific situation) and macro (e.g. general situation) improvement. FST is proposed as the logical progression of problem solving strategies in educational systems. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".