Sanitation workers and ‘structural racialisation’ in a globalising Centro Havana
Bibliographic record
Abstract
Discussing racism as more than just a past legacy has been taboo during most of the Cuban Republic, including under socialism. But since the 1990s, theorists, often drawing on concepts from contemporary race theory in the US, have started to analyse racism as a continuing, active force in Cuba. The author takes issue with formulations of racism that treat it as a discursive force and disconnect the debate on race from class. Based on long stints of fieldwork in Cuba since 2015 with a workforce almost automatically associated with blackness – the workers in the sanitation department of Centro Havana – the author argues for attention to the forms of racialisation that most impact poor and working-class Cubans and that are part and parcel of capitalist penetration and the impact of globalisation. Using Arun Kundnani’s ‘darker red’ theorisation of ‘structural racism’ as a systemic, albeit shapeshifting, companion of global capitalism, this piece explores how the specific capitalist dynamics unfolding in Havana today introduce the spectre of structural racism. This means for the lives of these workers a shift from an everyday working experience of socialist integration to one of economic precarity; a shift from being socialised into the neighbourhood to becoming alienated from it; a shift from sanitation workers being targeted for ‘socialist civilising’ to them becoming subject to punishment. A key difference that Cuban socialism can make lies in how the authorities respond to workers resisting such structural racialisation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".