Representations of Labour, Race, and Orientalism in Tale of a Certain Orient and Blackbodying
Bibliographic record
Abstract
The present research paper analyses the novels Tale of a Certain Orient by Brazilian author Milton Hatoum, and Blackbodying by Canadian author Dimitri Nasrallah, regarding their depictions of the Lebanese migratory experience in two different settings: the Amazon region in the early 20th century, and Toronto in the late 20th century. Using the frame of labour and race relations, as well as analysing the use and rejection of Orientalist stereotypes, I highlight the similarities, but mostly the differences, between various experiences of migration of people originating in the same place. While the Brazilian novel shows us the social and economic ascent of a Lebanese family in the context of early 20th century Manaus, privileged by their position in relation to their local Black and Indigenous employees, the Canadian novel brings forth the hardships and isolation of a recent immigrant fleeing war and left entirely to his own devices in a hostile new environment. I conclude that despite their regional differences, the two novels resonate with universal messages that are relevant to many people’s experiences in a globalized world shaped by migration and displacement.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.029 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".