Caste and Colourism: Analysing Social Meanings of Skin Colour in Dalit and Savarna Discourses
Bibliographic record
Abstract
We know little about how skin colour is used to discriminate and dehumanise Dalits in everyday language. Thus, the construction of fairness and darkness of skin colour in savarna perception and the qualities attributed need to be under-stood through the lens of caste identities. Drawing on an ethnographic study in Nallapadu Palle Scheduled Caste Colony in Andhra Pradesh, this article aims to understand how various qualities are attributed to the skin and colour of Dalits and savarnas using Qualia, linguistic registers and indexicalities. The Telugu linguistic forms “Nalupu” (Dark) and “Telupu” (Fair), when used in registers, are analysed to understand the qualities indexed with these forms. It is essential to examine the process of caste manifestation in language through colour, which indexes several qualities through a specific linguistic form, varying its social meaning when attributed to a savarna and a Dalit. The study found that the social meanings of Nalupu and Telupu used in everyday conversations differed for savarnas and Dalits. When spoken in the context of Dalits in Palle, it indexed qualities to discriminate and re-establish caste. It is argued that these attributes lead to the creation of caste hierarchies. The article calls for examining the connection between colourism and caste discrimination further.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".