Problems in applying Soft OR methods to climate actions: lessons from two cases of governmental use
Bibliographic record
Abstract
Abstract The field of Soft Operational Research (Soft OR) has emerged from the attempt to address contextually rich, multi-actor ‘wicked’ problems that are not amenable to traditional ‘hard’ operational research techniques, which often rely on mathematical modelling. This study assesses the use of Soft OR techniques in climate change policymaking. Since climate change problems are classical wicked problems, many assume that Soft OR would be in high demand in developing climate change policy. And the review of the use of these techniques conducted here does find that in the cases where Soft OR methods have been used by academics and other non-governmental actors, they have consistently provided useful results for policymaking. It is puzzling therefore that there is little evidence of governments using Soft OR application in this area. We study two cases of explicit (in Bristol UK) and implicit use (Rhode Island, US) of such techniques by governments to explain why this is so. We argue that notwithstanding the challenges the two cases reveal in their application, Soft OR nevertheless has much to offer policymakers in the arena of climate change policymaking and deserve more attention and use.
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 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 | 0.037 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".