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Record W3199784777 · doi:10.1016/j.wasec.2021.100100

Water-related sustainable development goal accelerators: A rapid review

2021· review· en· W3199784777 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWater Security · 2021
Typereview
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsMcMaster UniversityUnited Nations University Institute for Water, Environment, and Health
Fundersnot available
KeywordsAcknowledgementSustainable developmentSustainabilityGender equalityPsychological interventionProcess managementBusinessWater sectorPolitical scienceComputer scienceEconomic growthEnvironmental economicsEnvironmental resource managementRisk analysis (engineering)EngineeringEconomicsSociologyWater supplyMedicineEnvironmental engineeringBiology

Abstract

fetched live from OpenAlex

The United Nations has adopted accelerators – policies or programs that target multiple SDGs – to expedite delivery of the Sustainable Development Goals (SDGs). This rapid review examines the potential application of accelerators in water interventions from 2015 to 2020, with special consideration of how gender is integrated to fast-track SDG implementation as a cross-cutting case. While 86% of water projects acknowledged SDG interlinkages, project indicators did not reflect SDG acceleration objectives. For example, despite widespread acknowledgement of gender as a critical SDG issue, only a fifth of projects applied gender-related accelerators, and the bulk lacked strategic gender dimensions that addressed systemic roots of inequality. This suggests a strategic opportunity for the water sector to accelerate SDG progress through greater integration of cross-cutting programming.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.844
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.029
GPT teacher head0.304
Teacher spread0.276 · 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