What's stopping sustainability?: examining the barriers to implementation of clouds of change
Bibliographic record
Abstract
Despite understanding the need to become sustainable, and knowing some of the actions required to reach this end, barriers exist that prevent individuals, and society, from adopting actions that support sustainability. To understand what some of these barriers are, the case of Vancouver's attempt to implement the 1990 Clouds of Change recommendations has been analysed. Councillors, civic staff, Task Force on Atmospheric Change members and citizens who participated in the Task Force's public participation process were asked to identify what they perceived as the barriers to action-taking by the City to implement the recommendations. Fifty-eight people were interviewed. The barriers identified fell within three categories: Perceptual/Behavioural, Institutional/Structural and Economic/Financial. Analysis reveals how the barriers functioned, which ones were perceived as causing the greatest impediment to implementation of the recommendations, what conditions facilitated implementation of some recommendations, and suggestions regarding how some barriers may be overcome in the future. The six most commonly cited barriers were: lack of understanding about the issues, perceived lack of empowerment, competing issues, inadequate funds, fear of losing constituent support and limitation of jurisdiction. Other important barriers were: differences in perception, inappropriate structure of government (vertical), weak linkages among the policies of civic and senior levels of government and weak communication linkages between government and its constituents. Many of the barriers identified contributed to a low degree of civic participation in the City. Suggestions for improving government effectiveness, in terms of its ability to implement the Clouds of Change recommendations focussed on ways of improving civic participation among citizens. Suggestions regarding the amendment of government structures and decision-making processes are also presented.
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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".