Inter-actor Trust in the Planning Process: The Case of Transit-oriented Development
Bibliographic record
Abstract
Inter-actor trust (or the absence of it) plays an important role in complex planning processes. Trust has received much attention in management science, but surprisingly little in planning literature despite the similarities between the two and its increasing importance in ensuring coordination between multiple, heterogeneous actors in delivering developments. This paper aims to explore the role of trust in coordination in transit-oriented developments processes, based on literature research and two empirical case studies in the region of Toronto in Canada and the province of Zuid-Holland in the Netherlands. This research suggests that in both planning contexts trust is an important element in achieving successful outcomes. Trust was often identified at a personal level as something which can bridge differences between organizations, but that can be hindered by a history of distrust between organizations. The building of trust between stakeholders seems dependent on a commitment to building a good relationship early and openness throughout. Breaches of trust, as long as they are not fatal for the relationship, can lead to a stronger trust relationship in the long term. Trust, however, is not just an individual or organizational matter: the broader institutional context was also found to have pronounced impacts on the ability of trust to take root.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".