“The Function of Urban Space in "In the Skin of a Lion" by Michael Ondaatje”
Bibliographic record
Abstract
The slogan of Tourism Toronto today “The World in One City” explicitly signifies the city’s multinational nature, its new identity as a “polyphonous city of many cultures” (Rosenthal 2011, p.32). The city of Toronto, its history in the first half of the twentieth century and the numerous people behind that history, whose names will remain either unknown or long forgotten, play a substantial role in Michael Ondaatje’s novel In the Skin of a Lion. The space of the city is not only an important background where the majority of the events unfold for the reader, but it is also a driving force in both the construction of the story’s narrative and the development of the characters. Drawing mainly on the works of Henri Lefebvre and Edward Soja, this paper investigates the function of urban space in Ondaatje’s novel In the Skin of a Lion, and further contemplates the significance of urban space in modern literary texts. The urban space incorporates several functions in the text. On the one hand, the multifaceted character of the city’s vast space isolates and alienates the individuals who live there. On the other hand, it unites the characters in a paradoxical way and bonds them together to a certain degree. Furthermore, the urban space is constantly produced by the people who live there; and every character in the novel constructs their own space of Toronto and shares their individual space of the city with the reader.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".