Lyotard’s Notion of Metanarratives in High Muck a Muck: Playing Chinese
Bibliographic record
Abstract
High Muck a Muck: Playing Chinese is a digital poem, but an interactive experience. Developed through an interdisciplinary effort of eleven Canadian artists, programmers, and community members, the project comprises an interactive website, eight videos, and a gallery installation. The digital text explores the theme of Chinese immigration to Canada’s West Coast, highlighting both historical and contemporary issues faced by diasporic communities in the host country. This research examines the work through the postmodernist framework of French theorist Jean-François Lyotard, particularly his claim regarding the demise of metanarratives or grand narratives. Postmodernism is marked by scepticism towards established beliefs and absolute truths. Lyotard challenges the validity of Western metanarratives, arguing that such grand narratives have lost their authority in the postmodern world. As High Muck a Muck: Playing Chinese engages deeply with diasporic issues, it implicitly rejects dominant narratives surrounding immigration to Western societies. Like other digital texts, it incorporates texts, images, videos, and sound, all of which will be analysed through Lyotard’s lens to support the argument for the death of metanarratives. The text confronts and critiques prevailing narratives of multiculturalism, racial harmony, materialism, and economic prosperity in Western, particularly Canadian, contexts.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.052 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".