Local Integration Ontological Model of Creative Class Migrants for Creative Cities
Bibliographic record
Abstract
An innovative creative class drives creative cities, urban areas in which diverse cultures are integrated into social and economic functions. The creative city of Chiang Mai, Thailand is renowned for its vibrant Lan Na culture and traditions, and draws new migrants from other areas in Thailand seeking to become part of the creative class. This study aims to classify a local integration model for the migrant creative class, and to suggest a set of indicators that could be used to measure the level of successful integration of a migrant creative class when building creative cities. This study selected twelve creative class sample cases who are well known in Chiang Mai and separated the sample into three groups; educator, researcher, and innovator. The study’s agenda consisted of open-ended questions with a semi-structured format for the in-depth interview, and follows a local integration ontology model. The study found that the local integration model consisted of four key domains: means and work, social connection, facilities, and foundations domains. Significant factors are included in each domain, and all four domains are important for the local integration of the creative class in creative cities. Annotation Ontology was used for determining the critical factors for success for migrants in creative cities: namely job, training, social bridge, and language and cultural knowledge. This study presents a new model, the Lan Na Local Integration Metric, which can be applied to understand the successful integration of migrants into the Lan Na region of Chiang Mai.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".