The Engaged Community: Trust-Building within Public Engagement toward Community Development
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.028 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".