Bibliographic record
Abstract
We ask a lot from a city: housing, trade, employment, arts, transportation, education, manufacturing and recreation. And then there are also more intangible demands like equity, opportunity, safety and health. These elements are organised, distributed and prioritised based on the cultural values of citizens. The word 'values' is interesting. As a noun in the singular, it refers to worth -but in its plural form, its meaning is tied to ethics and beliefs. The two words are inexorably linked, however, because our values influence what we value, and not only in monetary terms. Beyond exchange value there is use value, cultural value and productive value. But what does this have to do with cities? Values are fundamental to the built environment as a human artefact. This idea is reflected in the theoretical framework of Baukulture or building culture, which recognises that values are interwoven with physical form, and, more specifically, it speaks to the changing nature of shifting cultural processes. As built and designed spaces, cities displace or bury natural landscapes and systems, and the trade-off is increased exchange and productive value for the city and its residents -a bargain reflecting the values of the society involved. And though the subjugation of nature is a familiar historical trajectory for urban centres, this path dependence can be redirected or reimagined to reflect other values.
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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.135 | 0.096 |
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".