New Residential Districts Ordering: from Language of Smells Description to the Space Sensory Order
Bibliographic record
Abstract
The article analyzes the sensory aspects of urban life in one of the districts in the outskirts of Moscow. Revising the concepts of sensory ordering of space, I analyze the ways how urban dwellers in this district order the space by smell perception. I define three components of sensory ordering: i) the language of sensory experience, ii) ascription of meanings to space and smells localization, iii) actions aimed at supporting the desirable olfactory landscape. The process of creating this language and negotiations about the desirable olfactory landscape are based on adopting the special terms and visualizing the smells. This language allows to interact with the various agents on different levels of power, to transform and control the space. The production of meanings, based on smells, influences the district identity. Both positive and negative smell perception produce the value of the district, while the negative smells acts differently and devalues the other city territories. Citizens actions support the sensory normativity: through sensory patrolling (different ways of detecting the smells) and microordering (creating the cleanliness and freshness within neighborhoods). These ways of ordering sensory experience and creating olfactory landscape help to explain the interaction between district dwellers, the principles of how their agency is formed, and the principles of being responsible for the space.
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 | 0.001 | 0.002 |
| 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 | 0.012 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 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".