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Record W4412434569 · doi:10.18357/kula.253

Spinning Experience

2025· article· en· W4412434569 on OpenAlexvenueno aff
Pankaj Sekhsaria

Bibliographic record

VenueKULA knowledge creation dissemination and preservation studies · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicIndian History and Philosophy
Canadian institutionsnot available
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

While the spinning wheel, known in many Indian languages as the charkha, continues to be an integral part of modern India's imagination (Gandhi made it a symbol of self-reliance and freedom), very few people have actually seen the object, leave alone having worked with it. There is also a huge discussion on the (ir)relevance of this technology in modern times because it is hand driven, slow, and low on output and productivity. Is the charkha then an obsolete and irrelevant technology? Is it a slow and unproductive relic of the past that we remember only because of Gandhi or his central role in India's freedom struggle? Can it play any role at all in the many crises we are facing today? The Spinning Experience follows thirty students at the Indian Institute of Technology Bombay as they learn the slow and gentle art of hand spinning on the charkha in a three-day workshop which is part of a course I teach titled Technology, Society and Development. One sees a churn of the narrative as the workshop progresses—through a combination of discussion and debate but more importantly experiential learning and the possibility of creating tacit knowledge and understanding. Learning unfolds in unexpected ways. The yarn breaks regularly but then the rhythm begins to set in and new understandings are created for ideas of skill, labour, history, craft, and sustainability. The highlight of course is a small length of cotton yarn (one hundred to two hundred meters on average) students take with them as a real, tangible output of what they have created themselves. The idea of the film originated from my experience of conducting this three-day workshop for the preceding five semesters. The transformation one saw in the short span of the three days—students join the workshop for ninety minutes on three consecutive days each—was both quite remarkable and fascinating. There was on the one hand a demonstration of how learning happens by doing and then there is the output itself. By output I mean both the physical yarn that is created and also the experiences the students go through. Some of this was reflected in short assignments students subsequently wrote on the experience of doing the workshop. I felt this experience could/should be captured in real time and not only through a post-facto reflection. Filming the workshop as it unfolded offered that opportunity, and it helps that I am trained as a filmmaker. The approach was ethnographic and instinctive—I had a sense this could be a film, but that was not the plan and neither was I sure how it would turn out finally. I thought of a film only when I saw the call from KULA, and I would say in retrospect that my instinct about the possibility of a film did turn out to be right. It helped hugely of course that I found a willing and deeply empathetic collaborator in Pradeep Patil when he agreed to edit and actually put the film together. The few public screenings I have had of the film (all incidentally in educational institutions) have drawn an unexpectedly warm and positive response and given me much confidence and hope. We are also entering the film into documentary film festivals and while we do not know what the outcomes will be, we are hopeful it will be well received in this community as well.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.087
Threshold uncertainty score0.291

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0120.005
Scholarly communication0.0060.004
Open science0.0010.012
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0870.018

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.

Opus teacher head0.079
GPT teacher head0.388
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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