Read it and Laugh: Humour and Mistake-Making in Green Grass, Running Water
Bibliographic record
Abstract
In this essay, Samantha Elmsley perceptively illustrates how laughter generates accountability for non-native readers approaching Thomas King’s novel, Green Grass, Running Water through a settler-colonial critical framework. She suggests that when characters make humourous mistakes in the text, it reveals the underlying tensions between native and European discourses. Laughing at these mistakes can help non-native readers address their own complicity in settler-colonial/native power relations by acknowledging existing power structures, and, through laughter, undermining them. Bringing together a Bakhtinian approach to laughter with a Foucauldian analysis of power relationships, Elmsley shows how humour “frees readers from habitual interpretations” in order to “question their own subjective standpoint,” and make space for alternative native subjectivities and epistemologies. This essay won the 2013 Avie Bennett Prize for best undergraduate essay in Canadian literature. Emily Ballantyne
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".