Challenging narratives of confinement: diasporic (im)mobilities in Lọlá Ákínmádé Åkerström’s <i>In Every Mirror She’s Black</i> (2021)
Bibliographic record
Abstract
Many literary productions of the (new) African diaspora focus on Anglo-American settings, whereas diasporic communities outside the United States, Canada or the United Kingdom are often neglected. Lọlá Ákínmádé Åkerström’s In Every Mirror She’s Black (2021), in contrast, centres the journeys and experiences of three Black women in Sweden to interrogate its gendered and racialised politics of migration, (social) mobility and belonging. Drawing on Levine’s notion of social structures as infrastructural, this article reads the novel through the lens of (im)mobility to illustrate how the intersection of gender, race, class, religion, nationality and legal status affects the literal and metaphoric movements of the protagonists. It thereby highlights the intricate relationship between (social) infrastructures, such as stereotypical or essentialist representations that function as “controlling images,” and the spatial and metaphoric organisation of society. In playing with and transgressing generic boundaries, moreover, the novel’s poetics reflect the fluidity and mobility inherent to narratives of migration. Through its thematic engagement as well as its poetics, the novel complicates narratives of a universal diasporic experience and thus challenges narratives of (generic) confinement.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.027 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".