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Record W2538409198 · doi:10.13140/rg.2.2.15205.70880

Uncovering and mapping place attachment in small cities

2016· article· en· W2538409198 on OpenAlexaboutno aff
Nicole Vaugeois

Bibliographic record

VenueScholarWorks@UMassAmherst (University of Massachusetts Amherst) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPlace Attachment and Urban Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyComputer science

Abstract

fetched live from OpenAlex

Understanding the connections that visitors and residents have to places is critically important for tourism development. Aided with this knowledge, authentic experiences can be developed, stories can be uncovered and told, and resident perspectives can be identified. This paper describes a case study to uncover and map place attachment in three small cities in Western Canada. The project was conducted in the cities of Courtenay, Port Alberni and Nanaimo, BC. A one day “walk about” in each community was used to record 1.5 minute videos (n=85) of residents speaking about a place in their downtown core where they felt connected to. These videos were then uploaded to Arc GIS resulting in the first layer of a dynamic map for each community. Findings were analyzed using content analysis and data visualization techniques. The findings suggest cultural mapping practices can aid in the design and promotion of tourism experiences.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.030
GPT teacher head0.244
Teacher spread0.214 · 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
Published2016
Admission routes1
Has abstractyes

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