Bibliographic record
Abstract
South Asia is home to an incredibly rich variety of embroideries that include folk, courtly, ritual, and commercial traditions. The scholarly literature on South Asian embroidery has been meagre however and historically emphasized professional embroideries at the expense of the folkl. With few recent exceptions2 folk embroidery in the Sub-Continent has most often been described and classified without reference to the women who make it or the specific cultural traditions that support and give meaning to it3. These 'characterising' accounts of folk embroidery are likely the result of historic circumstance and ancient bias4 but they are also the result of certain methodological shortcomings. My research attempts to counter these shortcomings through the use of embodiment and focusing not strictly on embroidery, but embroidering-the processes involved, their contexts, change, and women's perceptions of meaning. Despite the growing influence of theories of praxis and embodiment on the social sciences5 and their demonstrated potential for furthering understanding of gender and textiles6, there has yet to be an experiential, embodied account of embroidery in South Asia. This paper will examine the potential of embodiment to further women-centred, praxis-oriented research, enhancing understanding of embroidery and women's relationship with it.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".