Institutional stakeholder interviews (Portland, Oregon) on the uncertainties, concerns and challenges the limit implementation of Blue-Green infrastructure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This is a qualitative data collection. These data were collected as part of an interdisciplinary project undertaken by the Blue-Green Cities (B-GC) Research Consortium with the Portland-Vancouver ULTRA (Urban Long-term Research Area) project (PVU), as part of the “Clean Water for All” initiative. The project examined the sources of uncertainty responsible for current concerns and challenges to widespread adoption of Blue-Green Infrastructure in urban flood risk management. The study consisted of eleven semi-structured interviews with institutional stakeholders in the City of Portland, Oregon, USA. Broadly, the research aim was to identify and classify the key concerns, challenges and uncertainties faced by the interviewees in implementing sustainable flood risk management and Blue-Green infrastructure. We used the Relevant Dominant Uncertainty approach and identified numerous physical science and socio-political uncertainties that hamper decision making. We then addressed how decision makers can reduce their levels of concern and overcome the associated challenges to widen the implementation of Blue-Green infrastructure.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it