Bibliographic record
Abstract
Khare and Varman present a compellingly pessimistic analysis of the plight of the poor in India. The dilemmas of the poor are often exacerbated by large corporations seeking to find ways to market products to impoverished emerging market consumers. In India, consumers are frequently hurt by these initiatives, small retailers may suffer, while corruption and trickery by petty bureaucrats and ruthless landlords help the rich get richer at the expense of the poor. The article by Khare and Varman is a scathing indictment based on detailed ethnographic evidence but it reveals only a fraction of the disadvantages and traps of disempowerment facing those Indians living lives of great precarity. In this comment, we seek to build upon Khare and Varman’s insightful analysis both in order to reinforce their conclusions about the Kafkaesque existence of India’s poor and to introduce some further considerations and complications that make the quagmire even more entrapping. We focus on four sources of these problems: patriarchy, bureaucracy and corruption, class and caste power and hierarchies, and uneven and inadequate infrastructure. We also highlight some largely individual and non-government initiatives that may offer hope of escaping this quagmire for the poor.
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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 | 0.002 | 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 | 0.012 | 0.014 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".