Masculinities and the Aesthetics of Love: Reading Terrorism in<i>De Niro's Game</i>and<i>Paradise Now</i>
Bibliographic record
Abstract
This article examines Rawi Hage's (2006 Hage , R. ( 2006 ). De Niro's Game . Toronto , Ontario , Canada : House of Anansi Press . [Google Scholar]) De Niro's Game and Hany Abu-Assad's (2005) Paradise Now for their capacity to give us insight into the meanings of racialized masculinities. Neither text represents a very consoling picture of men in war and conflict, but they have a great deal to teach us about the fragility that underpins masculinity in volatile political contexts. Indeed, they give us insight into the affective realities of racial traumas that inhabit our constructions of identity, ideological positionalities, and cultural representation. Inspired by Frantz Fanon's (1952 Fanon , F. ( 1952 ). Black Skin, White Masks . New York , NY : Grove Press, 1967 . [Google Scholar]) plea for a new humanism and Paul Gilroy's (2005 ——— . ( 2005 ). Postcolonial Melancholia . New York , NY : Columbia University Press . [Google Scholar]) assertion that we attend to and politicize human suffering, I propose a psychoanalytic aesthetics of loss as a model for understanding and renewing cultural and political life. My method demands that we recognize that aesthetic cultural texts have an emotional source and that “being touched” by affect might teach us how to become better readers of our time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.025 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".