Bibliographic record
Abstract
This paper proposes a general model, based on what is known about the nature of (complex) systems, of how systems-in particular, health care systems-respond to attempted change. Inferences are drawn from a critical literature review and reinterpretation of two primary studies. The two fundamental system-change approaches are "stipulation" and "stimulation": stip(ulation) attempts to elicit a specific response from the system; stim(ulation) encourages the system to generate diverse responses. Each has a unique strength: stip's is precision, the ability to directly impact the desired outcome and only that outcome; stim's is resonance, the ability to take advantage of behavior already present within the system. Each approach's inherent strength is its complement's inherent weakness; thus, stip and stim often clash if attempted simultaneously but can reinforce each other if applied in alternation. Opposite patterns (the "stip-stim spiral" vs "stip-stim stalemate") are observed to underpin successful vs failed system change: The crucial difference is whether decision-makers respond to a need for precision/resonance by strengthening the appropriate approach (stipulation/stimulation, respectively), or merely by weakening its complement. With further validation, the model has the potential to yield a more fundamental understanding of why system-change efforts fail and how they can succeed.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".