Firm systems thinking: unifying educational problem solving
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it