Bibliographic record
Abstract
From the very beginning of my writing on the regime of informality I have rejected the notion of a dichotomy between formal and informal labour relations. The fracturing solidifies a differentiated absorption in the labour process – own-account workers versus waged labourers, regular against casual employment, replacement of sedentary engagement in paid work by footloose mobility – and all of this culminating in divergent patterns of livelihood and lifestyles. It is along these lines that I have split up “informality” class-wise, following up on the contention that rather than juxtaposing the working class as an amalgamated lot, there are indeed diverse classes of labour with distinct identities. The way in which differentiation has come about cannot only be comprehended in terms of social class-based alignments but also finds expression in an axis of steep inequality. It is a ranked order taking the shape of a class–caste nexus and makes clear how corresponding trajectories of accumulation and dispossession operate in tandem. The backdrop to this essay is the process of informalisation pushed by the stakeholders of globalised capitalism from the early 1970s onwards. The shift away from the regime of formality which used to be enjoyed by a minor segment of India’s mega-workforce has in many instances ended their privileged employment, legal protection and social security, tearing up the domains in which labour moves around.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".