Power interplay between actors: using material and ideational resources to shape local adaptation plans of action (LAPAs) in Nepal
Bibliographic record
Abstract
Deliberation over how to adapt to short or long-term impacts of climate change takes place in a complex political setting, where actors’ interests and priorities shape the temporal dimension of adaptation plans, policies and actions. As actors interact to pursue their individual or collective interests, these struggles turn into dynamic power interplay. In this article, we aim to show how power interplay shapes local adaptation plans of action (LAPAs) in Nepal to be short-term and reactive. We use an interactional framing approach through interaction analyses and observations to analyse how actors use material and ideational resources to pursue their interests. Material and ideational resources that an actor deploys include political authority, knowledge of adaptation science and national/local policy-making processes, financial resources and strong relations with international non-governmental organizations and donor agencies. We find that facilitators and local politicians have a very prominent role in meetings relating to LAPAs, resulting in short-termism of LAPAs. Findings suggest that there is also a lack of female participation contributing to short-term orientated plans. We conclude that such power interplay analysis can help to investigate how decision making on the temporal aspects of climate adaptation policy takes place at the local level.Key policy insights Short-termism of LAPAs is attributed to the power interplay between actors during the policy design process.Improved participation of the most vulnerable, especially women, can lead to the preparation of adaptation plans and strategies focusing on both the short and long-term.It is pertinent to consider power interplay in the design and planning of adaptation policy in order to create a level-playing field between actors for inclusive decision-making.Analysis of dynamic power interplay can help in investigating climate change adaptation controversies that are marked by uncertainties and ambiguities.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".