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.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.005 |
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".