Le déplacement de l'ironie dans Green grass, running water de Thomas King et In the skin of a lion de Michael Ondaatje
Bibliographic record
Abstract
Rewriting previous texts or drawing on past and contemporary sources in order to assert both an identity as well as a discursive position illustrate the ironic process at work in Thomas King's Green Grass, Running Water and Michael Ondaatje's In the Skin of a Lion . The two authors resort to intertextuality as a pre/text to revisit the past that has disregarded, if not written off, other protagonists' perspectives (the Native's and the migrant's) in the making of history. By including references to Melville's Moby-Dick , among many other allusions to literary and filmic works, King presents the Native's self-assertion in the history, of the Americas from the colonizing mission onwards. The focus on Moby-Dick as a doubly significant intertext (because of its biblical and historical content) enables one to examine King's strategic use of irony to simultaneously disrupt the colonial discourse and insert the Native's voice. Sometimes close to Melville's critique on the American society and European colonial attitude, sometimes more eager to re-authorize the Native's standpoint, King achieves a displacement of irony that brings to the fore the Native's text, a text that has remained invisible to history and fiction albeit omnipresent. Ondaatje merges references to The Epic of Gilgamesh into his fiction within an historiographic approach, indebted to John Berger's G ., to articulate in words the building of Toronto from the migrants' position. His epigraphs from the Mesopotamian myth and Berger's G . announce the relationship between building and narrating as a way to make visible the migrant workers' stories and voices. Displacing the discourse from men-in-power to migrant workers, Ondaatje relates irony as inclusive of both the said and the unsaid to the migrants' invisibility in the Canadian society.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".