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Record W4210279511 · doi:10.1079/tourism.2022.0007

Place-making: From a Residents’ Initiative to a Participatory Effect: The Case Study of BOMBAST

2022· article· en· W4210279511 on OpenAlexaboutno aff
Monique Schulte, Nicoline de Heus

Bibliographic record

VenueTourism Cases · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsnot available
Fundersnot available
KeywordsLogo (programming language)ExcellenceCitizen journalismSociologyPlace makingMaking-ofPublic relationsPsychological interventionMedia studiesAestheticsPolitical sciencePsychologyAdvertisingArtEngineeringBusinessLaw

Abstract

fetched live from OpenAlex

Abstract This case study illustrates an organic bottom-up place-making process, which started with the initiative of a couple of residents who created an invented folktale as a gift to their community. Through the development of a fictional character, these residents intuitively tried to embody the place’s norms and values or, in other words, the DNA of the community. The challenge, however, was how to transform this single initiative into shared meaning-making, borne by the wider local community. Two trends, noted in place-making literature, are of significance for the case. First, the case shows that, in making the place more attractive to live, the focus should be on the intangible attributes— the cultural soul of the place. Secondly, the case illustrates, through the use of the Imagineering Design Methodology, how the wider community has been enabled to become co-producers and co-consumers in the place-making process. Moreover, by actively including disruptive voices, such as those locals who were critical in the enabling interventions, the place-making process evolved. The case, therefore, shows how these interventions led to a participatory effect. VIU logo WLCE logo Information Vancouver Island University World Leisure Centre of Excellence Main image: Stichting BOMBAST, 2020. Drawing by Clema van Bekhoven © Monique Schulte – van Alphen and Nicoline de Heus2022

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.061
GPT teacher head0.321
Teacher spread0.260 · 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.

Study designObservational
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

Citations0
Published2022
Admission routes1
Has abstractyes

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