Integrated Evaluation Framework for Irrigation Development in South India
Bibliographic record
Abstract
The term engineer is no longer restricted to a technology developer pushing the envelope of productivity, innovation, and efficiency. Instead, engineers are now expected to address global issues while ensuring socially, economically, and environmentally sustainable solutions. In order to meet this demand, engineers need to move away from traditional technology focused development approaches and pursue the role of a ‘global engineer’ with the ability to apply multi-disciplinary skills, understand complex socio-econo-political interactions with technology, and work in cross-cultural environments.This research investigates the multi-disciplinary needs of the engineering community in the development sector, the skills and knowledge required to effectively address limitations of data availability and access, considers social and economical indicators of success, as well as address the engineer operating in a different culture and language. An irrigation scheme in south India was evaluated with social, economical, and technical measurement indicators instead of depending upon a solely technical approach. This approach led to the discovery of more factors influencing the success and failure of the irrigation scheme. With a more holistic evaluation, the potential for a more appropriate design for users; a better strategy for implementers; and the introduction of improved monitoringmechanisms for donors are made possible.
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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 | 0.034 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".