Decode the City: A Methodological Study Responding to the New Trend of City “Re-image” Montreal as a Case
Bibliographic record
Abstract
Recently, world-class cities start to evolve their city image into city impression through a city “re-image” process, to meet the new demands of city identity. The purpose of this study is to provide a theoretical method and practical strategies for creating city impression, and these can be utilized as basic tools for city management. The paper reveals publicly accepted key elements as signatures of a city, through the investigation of the relationships between exoteric features and cognitive identities of a given place, based on visual aspects at the neighborhood level. The city of Montreal, Quebec, a world-class city, is chosen for a case study. A comprehensive analysis of Montreal’s city impression is presented as a “decoding” process. The results show practical implementations which fit to the city’s context. The research framework assets providing a chance for planners to re-consider the needs of cities today and suggests intervention strategies to promote city impression.
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 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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.022 | 0.019 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".