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Record W2220984266 · doi:10.2495/sdp-v10-n4-579-589

Going more private and sustainable: ex-post assessment of armenian water utilities

2015· article· en· W2220984266 on OpenAlexvenueno aff
Naira Harutyunyan

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2015
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsArmenianBusinessEnvironmental planningNatural resource economicsWater resource managementEnvironmental scienceEconomicsHistoryAncient history

Abstract

fetched live from OpenAlex

Recent worldwide changes in water policies emphasize the role of the private sector in the provision of water services with the expectation of market forces to redress public provision failures and introduce innovative approaches for promoting sustainability.Armenia has experienced unprecedented rapid and mass privatization in the water sector: in a decade from zero reaching 63% of the population, which records the third highest level in Europe.The paper examines the impacts of privatization on sustainability performance of all water utilities.Ex-post benchmarking is employed for assessing relative and absolute sustainability measures and developing scores for utility sustainability ranking.The paper focuses on utility performance in time and scale dimensions and on the international level.The paper shows that transition to the public-private partnerships positively influenced the sustainability performance of all utilities.All utilities have improved their relative to pre-privatization performance.Considerable progress was seen in social followed by environmental performance.Armenian utilities also succeed in performing well internationally.The paper concludes that though water privatization may lead to sustainability of utility performance, the scale of impact may depend on the initial state of the enterprise and the local context.Moreover, after the low-hanging fruits are reached at the first stage, more efforts will be required for enhancing long-term sustainability and effectiveness, consistent with social and environmental needs.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.238
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2015
Admission routes1
Has abstractyes

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