6 How to Draw a Haunted Nation: Colonial Ghosts and Spectres in Conceição Lima’s Poems
Bibliographic record
Abstract
Ghosts are powerful presences in the narratives of countries addressing violent pasts. 2 The phantoms of slavery, 3 the ghostly presences of Indigenous peoples in the United States and Canada, 4 Aboriginal spirits of Australia, 5 Cameroon's wandering subjects, 6 the revenants of the Spanish Civil War and the Portuguese Colonial War, 7 the spectralization of Myanmar, 8 the phantasms of the great Chinese famine 9 and the spirits of the liberation struggle in Guinea-Bissau 10 -are spectral manifestations of violence.Most of these 'spectro-geographies', as Jo Frances Maddern and Peter Adey (2008) named them, are somehow related to a far from overcome colonial past and so they refer not to a dead and finished experience but are instead a product of the ongoing legacies of brutal imperial systems in newly founded nations.11 As 'the idiom of haunting' 12 is present across diverse geographies and substantially different colonial and postcolonial 13 histories, ghosts (and their interpretations) have to be culturally specified, as Esther Peeren states (2009, 2010).María del Pilar Blanco and Peeren are alert to the Eurocentric and ahistorical biases of Jacques Derrida's conception of the revenant in his Specters of Marx (1994), and so argue for a 'careful contextualization and conceptual delimitation' (2013: 15) of spectres.Taking its cue from their observation, this chapter focuses on the ways in which ghosts of the Massacre of 1953, in Conceição Lima's writings, help to unveil histories, voices and a profoundly shattered Santomean society that remains haunted by the consequences of colonialism.My argument is that, in these cases, spectres allow for renewed ways of imagining and telling the nation, in relation to both former colonies and metropolises.These ghost stories reveal as much about these nations' emergence from independence struggles as they do about their former imperial colonizers.
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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 | 0.002 | 0.005 |
| 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 | 0.023 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".