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Record W2788223312 · doi:10.11575/prism/5475

The Engaged Community: Trust-Building within Public Engagement toward Community Development

2018· dissertation· en· W2788223312 on OpenAlexaboutno aff
Srimal Isaac Ranasinghe

Bibliographic record

VenuePRISM (University of Calgary) · 2018
Typedissertation
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsPublic engagementCommunity engagementCommunity developmentPolitical sciencePublic relationsSociology

Abstract

fetched live from OpenAlex

This phenomenological inductive study addresses the issue of trust-building within the process of public engagement toward community development. The proposed engagement framework drew on data collected in the community of Marlborough, situated in the western Canadian city of Calgary, the Trust Confidence Cooperation (TCC) Model of cooperation, and the theories of social capital, equity planning, and complex systems. Key findings that emerged during the study indicate that trust and social capital are important to the success of conventional engagement methods such as surveys/questionnaires and open houses. The core attributes of a trust-building engagement process are positive outcomes, a relational approach, diversity, collaboration, physical presence, social capital, effective communication, customization, managing expectations, an adaptive approach, and dialogue. This study also proposes recommendations that address both process-level and systemic issues in the process of public engagement toward community development. Among others, these include: the need for adaptive governance structures that allow flexibility and customization, that community development processes be subsumed by a relational trust-building public engagement process, emphasizing cross-disciplinary collaboration, and managing community expectations through clear communication devoid of jargon.

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)0.022
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.429
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0390.001
Scholarly communication0.0000.000
Open science0.0040.001
Research integrity0.0000.008
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 head0.067
GPT teacher head0.281
Teacher spread0.214 · 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.

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

Citations1
Published2018
Admission routes1
Has abstractyes

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