Bibliographic record
Abstract
Critical linguists, including feminists, argue that language is not a value-free medium reflecting the world but a medium of constructing it. In every use of language, writers have at their disposal a wide repertoire of options, albeit within a restricted set of parameters. The selections they make are calculated and signpost ideological positioning. Stylistics offers a systematic approach to the analysis of language use and the description of ideological positions and three of its ambitions have been identified: to support existing interpretations of texts, to suggest new interpretations, and to establish general points about how meaning is made (Barry, 2002). In this paper, I demonstrate how stylistic analysis can be used to investigate women representation in texts and offer a feminist counter-reading of an existing interpretive claim. The analysis in question is Prabhat K. Singh’s interpretation of Indian Women by Shiv K Kumar, which he saw as a glorification of Indian women’s integrity, richness and faith. Singh also argues the women in the poem serve as a “metaphor for feminine beauty, chastity, patience, love and trust” (Singh, 2001, p. 107). However, detailed linguistic evidence reveals the tensions and inconsistencies in Singh’s reading, and demonstrates how his positive construction of Indian women is based a few selected details that do not allow a more thorough and coherent view of the poem. Stylistic analysis is used to demonstrate how a particular interpretation has been privileged and other interpretive possibilities downplayed, and provide an alternative reading sustained by a consideration of all aspects of the linguistic make-up of the text. The image resulting from the analysis is much less favorable than the one provided by Singh’s interpretation. Kumar has indeed constructed Indian women as powerless, inactive and silenced, thereby reinforcing traditional gender roles in patriarchal cultures.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.036 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".