The new ‘faces’ of the European metropolis and their Greek version
Bibliographic record
Abstract
The aim of this study is to present significant 'faces' characterizing the physiognomy of the contemporary Greek city as it is shaped or being shaped at the moment.In this context, the term 'city face' is used as an analytic tool, acknowledging that the image of the city is nothing less than the material and concrete expression of the really complicated, immaterial urban dynamics and trends.The main argument is whether the diverse 'faces' of the European metropolis are reproduced in the Greek city as well, simultaneously or with a delay; and whether the Greek city follows its own particular evolution, presenting similarities or differences from the European case.At a first step, corresponding views or aspects of urban image at the European or the global level are presented to create a wider reference framework.The European city 'faces' considered in this study are: gentrified city, multicultural city, sustainable city, virtual city, and the city of a special identity.The second step is to define dominant urban 'faces' for the Greek city as well: the historic city, the planned and designed city, the unplanned/ arbitrary city, the rural city, the marginalized city, the natural city, and the globalized city.Our analysis, summarizing the conclusions of recent research and studies, has shown that the revealed new 'faces' of the Greek city, although a result of widespread external dynamics, correspond to more or less common aspects of urban evolution; in fact, they are motivated by different agents and conditions in the Greek paradigm than in the European one.So the future evolution may be in some terms differentiated.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".