Intergenerational theatre in India: a reflective practitioner case study on an intercultural theatre exchange between Canada and rural Tamil Nadu
Bibliographic record
Abstract
In 2004, a Tsunami had devastating effects on the province of Tamil Nadu, India. In the community’s re-building process, many elders were forced to live in areas of the coastal region referred to as “Grannie Dumps,” because their homes were destroyed. With the monetary help of HelpAge International and the guidance of Michael Etherton, these elders are now part of an active, healthy community named Tamaraikulam Elders Village (TEV) that wants to tell its story. In March 2008, Michael Etherton attended a Workshop/Performance of GeriActors & Friends (G&F), an intergenerational theatre company from Edmonton, AB. I was G&F’s Assistant Director. After this, Etherton connected me with HelpAge India and TEV, realizing that the methods used with G&F would benefit TEV. Starting in January 2013 and completing in June 2015, under my direction, the University of Victoria’s Theatre Department assisted TEV in creating intergenerational theatre performance with various young people’s charity groups throughout the Tamil Nadu and Pondicherry region. The dissertation is structured as a reflective practitioner case study and is split into two sections. The first section of my work will communicate to the reader the events of the case study in India. The latter half of this work will be a collection of exegesis chapters reflecting upon the salient issues for the field of applied theatre research and practice which my research project brings up and how my reflections will affect my future practice while providing suggestions for how they could impact the entire field of applied theatre.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.040 | 0.015 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| 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".