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Assessing the Role of Social Capital in Promoting Water Conservation: A Study of Egyptian Universities

2024· article· en· W4405657714 on OpenAlexaff
Noha Elshaarawy, Abdelrahman Yousri

Bibliographic record

VenueAlexandria Science Exchange Journal · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in MENA
Canadian institutionsAmerican Water (Canada)
FundersAmerican University in CairoUnited States Agency for International Development
KeywordsSocial capitalCapital (architecture)BusinessEnvironmental planningNatural resource economicsEnvironmental scienceEconomicsGeographySociologySocial science

Abstract

fetched live from OpenAlex

This study investigates the role of social capital in shaping environmental responsibility concerning water issues across three Egyptian universities: Beni Suef, Ain Shams, and Alexandria. Using the 36-item Bullen and Onyx social capital scale, a survey of approximately 120 participants was conducted to assess whether social capital and demographic factors influence a sense of responsibility toward water conservation. Logistic regression analysis revealed that social capital was the only significant predictor of responsibility for water-related issues, while demographic factors such as gender, age, and qualifications did not have significant effects. Although the model as a whole was not statistically significant, these findings underscore the value of social capital in water management policies and strategies, suggesting it could play a key role in fostering community engagement and environmental stewardship. Future studies would benefit from a larger, more diverse sample that includes both public and private universities, as well as an enhanced social capital measure accounting for digital networks, income, and longitudinal data to strengthen the findings’ relevance and impact.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.790

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.002
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 head0.029
GPT teacher head0.337
Teacher spread0.308 · 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 designQualitative
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
Published2024
Admission routes1
Has abstractyes

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