Ethics in Performance of Violence: On Controversy of Collen Murphy‟s Pig Girl
Bibliographic record
Abstract
Canadian playwright Colleen Murphy's Pig Girl, prize-winning works of 2016 Governor General's Award and 2014 Carol Bolt Award, is based on a real criminal case and the increasing number of murdered and missing Native Canadian women.The play not only reveals a fact that women from indigenous communities in Canada are at high risk of violence, but also exposes the indifference of police force and the whole government.Despite the sympathy toward the deadly fate of aboriginal women, especially from a white playwright, the play still arouses strong objections and boycott from the Native communities.This contrasting reaction towards the play should merit due attention.This paper, with a comparison with other artistic works dealing with similar violent theme created by First Nations artists, tends to identify the difference and take some hints for future works.Only in this way, can a piece of work dealing with violence, especially violence suffered by generations of marginalized groups, contribute to the implementation of justice without getting involved in ethical argument, and eventually become an indispensable agency of intervention of social violence and curing of the historical trauma.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.055 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".