Cultural Sustainability and the Negotiation of Public Space - The Case of Indrachowk Square, Kathmandu, Nepal
Bibliographic record
Abstract
One of the major challenges today is to learn how to share spaces that have been made for all. This is not just relating to the use of old public space, it is also about creating new common space. Apparently, cultural, social and economic activities of certain societal groups influence access to public space, but these activities do not necessarily include all users or contribute to its overall sustainability. The aim of this article is to analyse how stakeholder negotiate and conduct activities, how these form and change, how they permit and confine access to public space for different users, in which ways they allow to negotiate access, and how they relate to sustainability with focus on a case study on local groups that are associated with Indrachowk Square, Kathmandu. Results of this study show that triple-bottom line sustainability is profoundly influenced by cultural activities. Further, there must be access opportunities for the various users of space. Access to space appears as dynamic process closely linked to negotiations about how to use it. In order to get and maintain access, competence development related to knowledge, values, feelings and cultural beliefs connected with the space plays an important role, and achieving this competence can be in turn encouraged by fostering specific cultural and sustainability related activities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".