Inclusion through modernity: Dravidian urbanisation in Tamil Nadu state, India
Bibliographic record
Abstract
Tamil Nadu state in India boasts high levels of economic growth, human development outcomes and urbanisation. How might we understand these concurring outcomes in this particular region? We propose that the Dravidian Movement’s vision for social and economic justice in the state centred a quintessentially spatial imaginary. Recognising that a socio-spatial ordering was central to the upkeep of traditional divisions of labour and associated hierarchies, Dravidian thinkers and leaders emphasised the need to transform the spatial order to create a more egalitarian society. The urban, in particular, was seen as a site of potential transformation, and urbanisation, as a way to break traditional caste-based oppressions and hierarchies. Until the 1980s, inclusion in Tamil Nadu was fostered through intentional, ideologically backed state processes to spatially involve many parts of the state in urban processes, and socially include multiple caste groups in turn, thus influencing urbanisation trends. In a contribution to scholarship on urban trajectories and the role of politics in spatial transformation, this article attempts to locate the role of political and ideological mobilisations in driving particular spatial and urban outcomes in Tamil Nadu state that are relatively different compared to the rest of the country. The critical differences between Nehruvian visions of modernity at the national level and those in Tamil Nadu, historical and geographical specificities of the region and the ideological and implementational bases of Dravidian mobilisation help explain the diffused – but not quite decentralised – urbanisation model in the state.
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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 | 0.000 | 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 | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".