Int ‘smart’:: cities (void) {If (equality ) { // ?
Bibliographic record
Abstract
‘Smartness’ is a socio-political tool restructuring the interpretation, infrastructure and behaviour of the city. In the prevalent rhetoric of ‘smart’ cities, which is characterised by apparent impartiality, disinterest, neutrality and objectivity, equality is rarely mentioned, interrogated, discussed or assessed. As shown by a series of ‘smart’ cities: Toronto—Google urbanism, Xinjiang —the ‘smart’ prison and Amaravati —the concrete on halt farm, ‘smartness’ does not stop inequality correspondingly; it can rather (often) perpetuate or increase it. Under the sharp shadows of the imperceptible algorithmic ‘smart’ logic, the paper will investigate power asymmetry, lack of accountability, transparency, the shortage of a civic debate and the lack of equality's weight in the ‘smart’ equation in prevalent ‘smart’ cities. Foreseeing the algorithmic inclusion in the cities must come with an integrated debate and policies on equality. In an age where digital ‘smartness’ parameters seem to drive urban decisions, this paper will question: Who are the people really benefiting? What is the value offered to society? How is it being discussed? Who is currently framing the urban 'smart' equality? In which instances equality is debated? By whom should it be discussed?
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.230 | 0.119 |
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".