Urban conflicts and the policy learning process in Hong Kong: urban conflict and policy change in the 1950s and after 1997
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.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".