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Record W2883387352 · doi:10.1111/aswp.12147

Civil society organizations and participatory local governance in Pakistan: An exploratory study

2018· article· en· W2883387352 on OpenAlexaff
Yeni Rosilawati, Zain Rafique, Bala Raju Nikku, Shahid Habib

Bibliographic record

VenueAsian Social Work and Policy Review · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsCivil societyCitizen journalismCorporate governancePoliticsPublic administrationLocal governmentGovernment (linguistics)Exploratory researchLocal governancePublic relationsSociologyPolitical scienceManagementEconomicsSocial scienceLaw

Abstract

fetched live from OpenAlex

Abstract This paper analyses the extent to which the civil society organizations (CSOs) have influenced the decision‐making of the local government toward the needs and priorities of citizens. In development discourse, civil society organizations hold a significant importance as they are deemed to provide holistic and new ways to ensure participatory local governance. Therefore, their role against the backdrop of their involvement in mobilizing citizens’ involvement and influencing decision‐making in Pakistan calls for further research. This paper aims to fill this gap. Using qualitative research methods, the current paper appraises the role of CSOs in mobilizing public involvement in the decision‐making process of local government institutions of Punjab, Pakistan. The paper finds that the effectiveness of CSOs is very low due to various institutional and political constraints. Motivations for CSOs seeking citizen involvement have been instrumental in nature rather than motivated by participatory principles.

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)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.019
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0070.005
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.079
GPT teacher head0.456
Teacher spread0.378 · 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 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

Citations20
Published2018
Admission routes1
Has abstractyes

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