Bibliographic record
Abstract
In India, more than ninety percent of the workforce is informal. In spite of this enormous percentage of informal workers, these workers remain invisible to law and policy circles. One of the reasons for such exclusion and invisibility is the absence of unionism involving informal workers. In order to overcome this invisibility, informal workers are increasingly organizing into associations that are different from traditional trade unions. These organizations devise their strategies and their legal statuses in view of the atypical characteristics of informal activities. In this Article, I document some of these organizations of self-employed informal workers in India – their characteristics and functions. On this basis, I contend that these organizations offer a model for collective action by informal workers. I argue that these associations are a sui generis organization of informal workers, and could become a precursor to solidarity-based collective initiatives by informal workers globally. In the backdrop to this proposition, I analyze the role of law in promoting such aggregation of informal workers in furtherance of their collective action. I argue that while organizations of informal workers in India employ the existing legal framework to the best of their advantage, the law fails to recognize some of their status as workers, thereby creating hurdles towards informal workers’ collective action. A small farmer works on her own farm. In tough times, she also works on other farms as a laborer. When the agriculture season is over, she goes to the forest to collect gum and other forest produce. Year round, she produces embroidered items either at a piece rate for a contractor or for sale to a trader who comes to her village to buy goods. Now, how should her trade be categorized? Does she belong to the agricultural sector, the factory sector, or the home-based work sector? Should she be categorized as a farmer or a farm worker? Is she self-employed or is she a piece-rate worker? Because her situation cannot be defined and contained neatly in a box, she has no work status and her right to representation in a union is unrealized. She is denied access to financial services or training to upgrade her skills. The tyranny of having to belong to a well-defined “category” has condemned her to having no “identity.” — Ela R. Bhatt E la R. B hatt , W e A re P oor but S o M any : T he S tory of S elf -E mployed W omen in I ndia 17 (2006).
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.029 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| 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".