A Framework for Analyzing Information Flows in Public Policy Decision-making: A Move towards Building Sustainable Policy Instruments
Bibliographic record
Abstract
The purpose of this paper is to explain a framework that focuses on information flows as a means of understanding public policy decision-making, with a specific emphasis on information relating to sustainable development. The goal of this framework is to further aid in identifying and explaining the extent to which sustainability goals are being implemented in public policy decisions. The suggestion is that by focusing on the information flows directly related to sustainable information, instances can be isolated where specific pieces of information are not making their way to final decision-making processes, or alternatively, where new information interferes with sustainable information becoming part of the final decision-making process. This paper begins with an overview of the theoretical framework in the first part, and then gives an overview of how the framework can be applied to sustainability information. It concludes by suggesting the use of the framework can offer substantial insight into questions of sustainable policy development. While the approach described offers the potential to better understand organizational decision-making dynamics, the true value of the framework will lie in its future use to both predict and assess the relationship between sustainable outcomes and related decision-making processes.
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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 | 0.025 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.016 | 0.030 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".