Learning communities and sustainable social-economic city development
Bibliographic record
Abstract
The basis of this publication is the symposium "Learning communities and sustainable social- economic city quarter development", held at the 5th European Conference for Community Psychology (Berlin, September 2004). It describes research projects in different European countries involving experiences, communities and experts, in order to identify guidelines for the use of psychological science in a broad social context. Community psychologists and other experts of the field are studied putting into practice diagnostic tools like community profiles, actor and conflict analysis, and intervention tools like: moderation techniques for creative team work, citizen activation and participative planning, conflict mediation techniques, future factories, citizen juries, community action research and management of city quarters. These strategies and methods have the broader application of facing different problems of community development such as urban transformation processes, renewing programs of city quarters in degradation, conflicts between different groups of citizens, participative planning of new areas and institutions, processes of local agenda 21 and so on. In contrast to traditional investment programmes, the potential of citizen participation and the empowerment of people are stressed, giving a strategic and operational contribution. In fact, sustainable City Quarter Development, besides helping the economy, is largely dependent on human factors and the sheer volume gives evidence that in different scientific as well as applied areas a wide range of strategies and methods have been developed to manage these human factors in a professional way. The various contributions highlight the fact that participation and collective learning processes are tools for active citizenship. Following the proposed approach, participation is tackled as a tool for social empowerment, and the whole text focuses on an operator who can act as a "social catalyst", proposing this figure to politicians and local administrators. The term catalyst best expresses the functions we identify and the role attributed to this figure - an expert who can "bridge" the divide between the various social entities. Will anyone be willing to make use of this figure, as a catalyst to the local context? We do not claim to provide an answer, but we hope the volume shows the advantages to be gained for anybody attempting to do so.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.001 |
| 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".