Education for the Creative Cities: Awareness Raising on Urban Challenges and Biocultural Preservation
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".