Bibliographic record
Abstract
本文之目的是探索香港市区旧区尤其是油尖旺区贫穷集中的情况，分析市区旧区出现贫穷集中原因，希望能引起社会人士及有关政府部门的关注，避免市区旧区「贫民窟化」的出现。这研究对统计处九六年中期人口统计有关区议会及统计规划小区的统计作资料再分析，当中会集中探讨油尖旺区的贫穷状况及需要。研究的结果显示香港的贫穷问题相当集中于乡郊区及市区旧区，而在市区旧区贫穷状况更集中于地域相当细小的社区。贫穷小社区的特色以私人楼宇及以出租单位为主，共同租户的比例非常高，而居民中老人、单身男性及新来港人士的比例较高，在劳动特徵方面，居民多是零售、批发、出入口、饮食及酒店业的服务工人及建造业的技术工人。本文指出低价私营租务房屋的供应及需求的互动，及社区经济的特色是令贫穷地域集中的原因。最後，本文建议以社区发展的手法为这些贫穷的小社区，进行扶贫作初步的建议，避免市区旧区「贫民窟化」的出现。 This study is a secondary data analysis of the 1996 Population By-census to explore and explain the geographical concentration of poverty in the old urban area in Hong Kong especially in the Yau Tsim Mong District. It suggests that poverty problem in Hong Kong is concentrated in small geographical localities in old urban areas. The characteristic of these small localities is the high ratio of households sharing a quarter with other households. More elderly, unmarried male adult and new arrival residents can be found in these poor communities. Moreover, more residents are employed as service workers and construction workers. The study reports that the concentration of poverty is a result of the interaction between the supply and demand of the low-end rental market of private housing and the development and characteristics of the community economics. Finally, this study recommends using community development as a strategy to alleviate poverty in these localities, as to prevent gettoisation of these old urban areas.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".