Realisation Mechanisms for the Public Interest in Successful Urban Development Plans, with An Emphasis on Planning Theories of the Second Half of the 20th Century
Bibliographic record
Abstract
What is known today as urban planning is perhaps conceived at a time when urban growth and its associated issues had reached the point of endangering the public interest with an unfair distribution of resources. This situation prompted urban planners to intervene in order to mitigate the conditions, bringing about a relatively balanced status into the need-resource dialectics. public interest is a recurring underlying issue for planning, and hence the need for further investigations.The current research is an attempt to explain realisation mechanisms for the public interest. The adopted approach is qualitative, with document-based methods used for data collection. Examples of the urban revitalisation plan for Medellín city in Columbia, the regeneration of Regent Park district in Toronto, the Zuidas Vision Plan, and the strategic development plan for Sydney—all internationally supported and evidently successful—are studied and analysed. Combining planning theories and content analysis of successful urban development plans, the research results in a public interest realisation mechanism diagram rendering them as processes. The main criteria for this diagram are fair distribution of resources, attention to environmental issues, social inclusion, housing, employment, interests-seeking, power networks, social communications and interactions, prompting social activism, Consciousness, and ethical commitments, norms and values; all illustrated in a network of relations in order to help planners ad planning processes realise the public interest.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.045 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".