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Record W4293360673

The evolution and importance of 'rules-in-use' and low-level penalties in village-level collective action

2018· article· en· W4293360673 on OpenAlexaff
Brian Joubert, Robert Summers

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCollective actionAction (physics)Middle levelPolitical scienceEngineeringLawPhysicsPolitics
DOInot available

Abstract

fetched live from OpenAlex

In many parts of sub-Saharan Africa community water points are provided through external support in the form of enhanced boreholes fitted with hand pumps. The external agency supplying the improved water source commonly provides maintenance training and assists in organising a governance plan for the water point. Despite its apparent virtues the Village-Level Operation and Maintenance model still experiences high levels of water point failures, even where the technical training and material conditions are adequate. There has been relatively little investigation of the institutional factors that may influence the cases where villages successfully maintain their shared water source infrastructure. This research investigated five villages in central Malawi where communities had maintained their water point hand pumps for periods exceeding 10 years. The results point to the importance of informal institutions giving primacy to ad-hoc 'rules-in-use' that suit the local context, and adapting forms of free-rider sanctions that are typically minor, low level and triangulated with local norms and behaviours. The findings highlight collective action that is successful through day-to-day adaption and that serves to institutionalise cooperative behaviour through appeals to norms.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.005
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 head0.248
GPT teacher head0.494
Teacher spread0.246 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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