Bibliographic record
Abstract
Abstract Recent debates in urban studies and urban anthropology have revolved around the growth of neoliberal economies and their impact on postcolonial cities such as Bengaluru and invoke the phenomenon of the death of the commons. Rather than focusing on a dialectical existence of infrastructures, which suggests a life and death binary, in 2020, I turned my attention to the possibility of a life between and beyond these two binaries through the game of football and its place in Bengaluru. This essay is based on a study of two different types of football fields in Koramangala, Bengaluru, and through this exercise, it intends to examine a potential move towards the viewing of commons as sites of knowledge production for sport, culture, and the city. One of the key ideas around which urban commons are looked at in this essay is through an examination of Bengaluru as a postcolonial city, one which was supposed to uphold a Nehruvian vision, and its transformation into the Information Technology hub of India through a neoliberal turn in urban development. A major concern raised here with neoliberal models of urban development is how people who do not have the monetary capacity to access sports infrastructure end up playing in the postcolonial, neoliberal city.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".