Men, Masculinity and Nature in Rural Caste Society: Exploring Dynamics in Tamil Nadu
Bibliographic record
Abstract
This paper investigates the relationship between men, masculinity, and nature in a rural caste-based society, with a particular focus on Tamil Nadu. Masculinity, a socially constructed concept, varies across cultures and historical periods. Men, masculinity and nature relation is primarily looked from the perspective of Ecofeminism, which critiques the interconnected oppression of women and nature within patriarchal systems. On the other hand, ecomasculinity specifically examines how traditional ideals of masculinity influence men’s attitudes and behaviours towards the environment. Employing the Ecomasculinty lens, this paper intends to explore the subtleties of the relationship between men, masculinity, and nature in a caste-based society in Tamil Nadu. The study employs textual analysis. The text considered is Heat by Poomani, translated into English by N. Kalyana Raman. The analysis will closely examine landscape representation, ecological elements, masculinity and the portrayal of caste in the novel. The caste system is considered vital in the study, as it influences the characters’ relationship with the land and their perceptions of nature. Overall, the paper aims to comprehensively understand the complex relationship between masculinity and nature, considering the intersectionality and plurality of masculinities in different social and cultural contexts. By exploring the relationship between men, masculinity, and nature in the caste-based society of Tamil Nadu, the study brings out the impact of social structures on the environment and how men relate to it in the said context.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".