Urban conflicts and the policy learning process in Hong Kong: urban conflict and policy change in the 1950s and after 1997
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 paper considers how the dynamics of a series of conflicts influence policy making and learning from experience. Two different series of conflicts centered on housing and public space are described and compared. First is the series of crises around illegal squatter settlements and fires that resulted in the Squatter Resettlement Programme, which eventually became a broad-ranging public housing programme accommodating half of Hong Kong's population. Second is a series of conflicts around post-1997 restructuring of urban space and public housing, which produced a number of setbacks for government plans. The first conflicts are interpreted as producing a learning process where initial responses failed to resolve the problems, failures demonstrated by subsequent crises, prompting new initiatives, eventually resulting in a partial solution through the adoption of permanent multi-storey Resettlement blocks. The second set of conflicts has revolved around public perceptions of a tight government/property developer nexus that drives public policies in detrimental ways. While the current set of conflicts has not been resolved yet, this paper will consider whether a similar learning process can be discerned. As yet, it appears that the opponents of government policy have been learning from their successes more than the government has from their setbacks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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