The Unintended Negative Consequences of Government Actions and Initiatives in Selected Environmental, Social and Economic Domains: Opportunities for Co-construction Approaches
Bibliographic record
Abstract
Governments of all levels have been involved to various degrees in dealing with environmental, social and economic issues, e.g. the vulnerability of human activities, coastal communities and cities generalculture and industry.While many interventions have been aimed at improving the situation for the affected populations and their environments, it is also the case that many interventions have given rise to unintended Centre for Research on Settlements and Urbanism Journal of Settlements and Spatial PlanningJ o u r n a l h o m e p a g e: http://jssp.reviste.ubbcluj.roGovernments are frequently involved in dealing with major environmental, social and economic issues, often with the stated intention of improving the situation of population directly concerned.However, many government interventions have also led to unintended negative consequences.Using selected issues particularly relating to agriculture, coastal communities and adaptation to climate change and variability, a conceptual framework is first presented.This focuses on the types of unintended negative consequences as well as their underlying causes.Some of the underlying causes relate to the lack of governments' understanding of how people in different territories have different priorities and act accordingly.A major approach for improving this situation is to develop co-construction processes leading to the creation of policies, programmes and initiatives.Co-construction involves integrating the extensive knowledge of the many legitimate actors who frequently have not been involved by governments in the development of policies, programs and initiatives.This involvement can involve citizens or their representatives, and should include the whole range of legitimate interests in what is being discussed, planned and put into action.In this article, brief reference is also made to unintended positive consequences of government action, but the focus is on the unintended negative consequences of government action.The article is based upon a wide range of research projects involving the different authors, including sequences of research projects in both developed and developing countries as well as drawing upon results from the research literature.e
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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 | 0.051 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".