Bibliographic record
Abstract
Managing a public sector organization is a highly complex task involving multiple stakeholders coupled with informational and resource material flows. Decision making in such complex tasks, for example heath-care system, presents challenges. On one hand, the complexity of public sector organizations does not lend itself well to real-world trial and error approach. Practical, political, and/or ethical constraints often restrict any experimentation with many real-world phenomena such as medical decision-making, hazardwaste management, climate change, and so forth. On the other hand, most of the real-world “decisions and their consequences” are hardly related in both time and space, which makes learning even harder to occur (Hogarth, 1981; Sterman, 1989). Recent advancements in computer technology, together with developments in system dynamics simulation methods, provide a potential solution that involves design and development of the decision support systems to aid decision making in complex public sector systems (Qudrat-Ullah, 2005). In this paper we argue that system- dynamics-based interactive learning environments (SDILEs) could serve as an effective decision support system for public sector management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".