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Record W4415116629 · doi:10.17561/at.28.9034

Validación de apariencia de la Escala de Inseguridad Hídrica de los Hogares en poblaciones vulnerables del Gran Buenos Aires, Argentina

2025· article· en· W4415116629 on OpenAlexaff
Ianina Tuñón, Nazarena Bauso, Matías Maljar, Hugo Melgar‐Quiñonez, Olga P. García

Bibliographic record

VenueAgua y Territorio / Water and Landscape · 2025
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsMcGill University
Fundersnot available
KeywordsVulnerability (computing)Scale (ratio)Social vulnerabilityPerspective (graphical)PhenomenonFace (sociological concept)

Abstract

fetched live from OpenAlex

There is extensive evidence on the consequences of lacking safe water on the health and well-being of populations, which justifies the need to advance in the construction of new measurement instruments. The objective of this study is to evaluate the face validity of the HWISE Scale to measure water insecurity in Argentina. Four focus groups were implemented with 24 women residing in vulnerable areas of Greater Buenos Aires, to whom the scale was administered. The results reflect the breadth of dimensions of the phenomenon in the participants’ discourse, and the items’ wording was understood. The HWISE scale represents the phenomenon from the perspective of women in situations of social vulnerability but requires adjustments. The ítems are understood and cover the comprehensiveness of the phenomenon, although areas for improving its precision are identified.

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)0.006
metaresearch head score (Gemma)0.007
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 score0.062
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.262
Teacher spread0.258 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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