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Record W2781260667 · doi:10.5539/jel.v7n2p60

Education for the Creative Cities: Awareness Raising on Urban Challenges and Biocultural Preservation

2017· article· en· W2781260667 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Education and Learning · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban and spatial planning
Canadian institutionsnot available
Fundersnot available
KeywordsTRIPS architectureCreativitySustainabilityEnvironmental educationField tripDiversity (politics)SociologyGeographyPsychologyPedagogyPolitical scienceEcologyEngineeringSocial psychology

Abstract

fetched live from OpenAlex

Creative Cities are facing the big challenges due to the demographical, environmental and economic issues. In this study we considered to create the educational fieldworks inside the creative city and raise the awareness in youth about the importance of the biocultural preservations to sustain the city’s creativity and sustainability. Our participants were 10 international participants with different backgrounds and majors. The fieldwork trips were divided according to the ecosystems of Kanazawa City, in three main part: mountain areas, rives and forest areas and finally coastal areas. In each course students directly interviewed the local artists, craftsmen and shop owners and recorded about the importance of biocultural diversity to preserve the city’s traditions. Evaluation of the students were conducted according to the submitted reports with comparative analysis, and providing further recommendations. From the results, awareness level about the present issues was increased in each student, and they provided the recommendation according to the local issues. However, this time we did not considered the scientific background of all interviews, and all recommendations were given based on the opinions of the locals. To improve our methodological approach in our next studies we are going to develop approved survey instruments to record and analyse the data collected by students, and perform quantitative data analysis with second cohort research group to evaluate and confirm the outcomes of the field trips.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.829
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
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 head0.062
GPT teacher head0.323
Teacher spread0.262 · 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