Bibliographic record
Abstract
In recent years there has been increasing attention to sustainability in the construction literature. This paper contributes to this work by mapping legitimacy theory to existing frameworks intended to define the sustainability of water, sanitation, and hygiene (WASH) projects. Legitimacy has been shown to affect organizational continuity, which is similar to this paper's definition of the social sustainability of infrastructure, namely, that it continues to be used and maintained by end users over time. Past work has built sets of indicators that are used to assess sustainability. This paper describes some prominent and widely applied frameworks for sustainability in WASH. Specifically, the frameworks reviewed here are from or are based on publications from UN-Water, the World Health Organization, Water for People, Engineers Without Borders-USA, the Canadian International Development Agency, the U.S. Environmental Protection Agency, the U.S. Peace Corps, and the IRC Triple-S program. The components from this analysis then are mapped to the constructs of legitimacy theory. The frameworks are compared to previous results to see if the components of legitimacy known to be important in promoting sustainable infrastructure are present in sustainability frameworks. This will allow practitioners to fine-tune sustainability frameworks using empirically proven research results.
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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".