Voicing the Alternative in Michael Ondaatje's Fiction
Bibliographic record
Abstract
Abstract As well known, Michael Ondaatje, who was born in Sri Lanka (the old Colombo), moved to London in his prime and, then, to Toronto, becoming a Canadian citizen. In Ondaatje’s literary works there is a constant interweaving of voices: from colonial and postcolonial motifs to the use of geography, space, art and music, in order to subtlety suggest the underlining and pervasive theme of identity: in almost all his writings references to both the loss of identity in the colonial period and the atrocity of the civil war in Sri Lanka lead to a sort of image of the international bastard, as Ondaatje himself says in The English Patient. My paper, which is dealing with Ondaatje’s autobiography, Running in the Family, and the novels The English Patient and Anil’s Ghost, refers to other works by M. Ondaatje as well. If Running in the Family is imbued with the idea of a split identity, with recurring references to Canada and Sri Lanka, both The English Patient and Anil’s Ghost have an international setting: Italy, Africa (the desert, Cairo and Egypt), England, India and Canada in The English Patient, Sri Lanka (but several other places in Usa and in Europe, evoked by memory) in Anil’s Ghost, thus suggesting the idea of the “International bastard”. In the two novels, art, music, voices and new technologies interweave with the theme of identity being metaphorically or allusively presented.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".