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Record W2128112265 · doi:10.5539/jsd.v7n3p129

Cultural Sustainability and the Negotiation of Public Space - The Case of Indrachowk Square, Kathmandu, Nepal

2014· article· en· W2128112265 on OpenAlexvenueno aff
Bijay Sıngh, Martina Keitsch

Bibliographic record

VenueJournal of Sustainable Development · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal socioeconomic and cultural dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityNegotiationStakeholderCompetence (human resources)Space (punctuation)Public relationsPublic spaceBusinessSociologyPolitical scienceSocial psychologyComputer sciencePsychologySocial scienceEcology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score0.515

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.259
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2014
Admission routes1
Has abstractyes

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