Male Vulnerability and Victimization: Examining Vassanji's No New Land
Bibliographic record
Abstract
This study examines how male vulnerability and victimization manifest in M.G. Vassanji’s novel No New Land as systemic outcomes of racial capitalism, neoliberal labor regimes, and intersectional precarity. It also analyses how Tanzanian masculinities undergo shift in M.G. Vassanji’s No New Land. The migration to Toronto exposes diasporic men to intersectional precarity specifically the collapse of patriarchal authority under racial capitalism and neoliberal labor regimes. Through Fanonian and Bourdieusian lenses, this paper studies Nurdin Lalani’s systemic disempowerment as a breadwinner to menial laborer epitomizes structural vulnerability, Jamal’s compensatory hypermasculinity in response to systemic emasculation, Nanji’s academic identity negotiation within a racialised job market, and Esmail’s direct confrontation with racial violence and its institutional consequences. It reveals how Vassanji’s Toronto, a space of symbolic violence brings down migrant men’s labor to feminized, racialised servitude, fragmenting static ideals of masculinity inherited from Dar es Salaam. The paper reconceptualizes diasporic masculinity as both a site of trauma and adaptive resistance, challenging narratives of patriarchal resilience in diaspora writing. Drawing on recent research, this paper synthesizes contemporary migrant mental health study to derive policy recommendations for tackling the mental health burden of intersectional erasure on migrant men in Western host countries. This paper contributes to discussions on non-Western masculinities in global migration studies. Furthermore, the argument challenges singular narrative accounts of patriarchal resilience in diaspora literature.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".