Assessing the effectiveness of water and sanitation sector governance networks in developing countries: A policy analysis framework
Bibliographic record
Abstract
In developing countries, water and sanitation services for rural and peri-urban areas often are provided by networks comprised of governmental and non-governmental actors.The resulting governance systems are rarely evaluated, in part because the methods to do so are complex and unclear.This paper builds on network governance theory to (a) propose a new framework for the assessment of the effectiveness of Water and Sanitation governance networks in developing countries and (b) apply it through field research in Honduras.Network theory suggests that, since the sum of the network is greater than its individual parts, the effectiveness of a network should be evaluated based on the performance of the overall network rather than that of its individual network actors.The proposed assessment framework starts with this premise and evaluates overall network effectiveness in the four stages of the policy process: policy development; policy decisions; implementation; and monitoring & evaluation.For the case of Honduras, performance indicators were specified for each policy stage, and an assessment conducted of the overall network's performance.Key findings from the assessment relate to the importance of metagovernance coordination functions, dramatic expansion of services, and key gaps in network integration.The research, and the assessment framework, will be of interest to those concerned with the effective delivery of basic services, particularly to secondary cities of the developing world where, as in Honduras, governance network commonly provide services and data for assessment are not yet compiled.
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 | 0.029 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".