Bibliographic record
Abstract
My essay focuses on the marginalised people whose livelihoods depend on gathering, sorting, transporting and selling garbage in India's huge informal economy, livelihoods now challenged as municipal governments contract the recycling of waste to corporations. The evolving, bumpy geography of the waste economy creates permanent border areas of primitive accumulation and both devalorised and valorised people and places. I make a case for understanding informal sector activities, such as the work of transforming the city's detritus, as part of a vast infra‐economy and the varied forms of labour performed within heterogeneous value chains of waste transformation as infrastructural labour that produces what Marx called capital's ‘general’ and ‘external’ conditions of production. Through close examination of the spatiotemporal lattice of informal municipal solid waste recycling, I demonstrate how these economies are at once highly organised and brittle, with each node in their value chains subject to disruption by state and market forces. While relative opacity, labour intensity of tasks and dependence on embodied knowledge (metis), indeed a ‘bodily’ feel for space, give informal economies the capacity to resist external efforts to transform, subsume or eradicate them; lack of social security and employment protections also means that workers and micro‐enterprise owners within them inhabit the thin line between survival and failure, rendering them vulnerable to economic and political fluctuations. The upshot is that the labour of waste and other informal sector workers is critical for maintaining the quality of life desired by the well off in cities of the global South, but fails to get the recognition it deserves. Waste workers are poorly compensated, regularly stigmatised and frequently invisible in policy decisions. This is an enduring inequity that demands urgent correction.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".