Participatory research for adaptive water management in a transition country. A case study from Uzbekistan
Bibliographic record
Abstract
Participatory research has in recent years become a popular approach for problem-oriented scientific research that aims to tackle complex problems in a real management context. Within the European Union project NeWater, stakeholder processes were initiated in seven case studies to develop approaches for adaptive water management. The Uzbek part of the Amudarya River basin was one of the studied river basins. However, given the current political and cultural context in Uzbekistan, which provides little room for stakeholder participation, it was unclear to what extent participation could be realized there. In this paper, we present an evaluation of the participatory research carried out in the Amudarya case study with respect to (i) the choice and application of different participatory methods and their adaptation to the given political, socioeconomic, and cultural environment, (ii) their usefulness in improving system understanding and developing strategies and measures to improve water management and monitoring, and (iii) their acceptance and suitability for enhancing policy-making processes in the Amudarya River basin context. The main lessons learned from the comparison of the different participatory methods were (1) the stakeholder process provided an opportunity for meetings and discussions among stakeholders from different organizational levels and thus promoted communication between different levels and organizations, and (2) in a context where most stakeholders are not generally involved in policy-making, there is a danger of raising expectations that a research project cannot meet, e.g., of transferring local interests to higher levels. Our experience shows that in order to choose participatory methods and adapt them to the Uzbek cultural and political setting (and most likely this applies to other post-Soviet transition countries as well), four aspects should be taken into account: the time required to prepare and apply the method, good information about the participants and the context in which the method will be applied, knowledge of the local language(s), and careful training of local moderators. While these aspects are relevant to any application of participatory methods, they become even more important in a political and socio-cultural setting such as that found in Uzbekistan. One added value of the activities and a crucial aspect of a participatory research processes was the capacity building of local scientists and practitioners, which facilitates the further application of the methods.
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 | 0.012 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".