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Record W1868047815 · doi:10.24908/pceea.v0i0.3607

Integrated Evaluation Framework for Irrigation Development in South India

2011· article· en· W1868047815 on OpenAlexaffvenue
Holly Lafontaine, Tirupati Bolisetti, R. Balasubramaniam

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsProductivityDisciplineWork (physics)Order (exchange)Engineering managementSustainable developmentEngineeringComputer scienceBusinessEconomic growthPolitical scienceEconomics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.213
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2011
Admission routes2
Has abstractyes

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