Building a Counter-Archive: Performing the Lost Histories of Migration and Exile
Bibliographic record
Abstract
In Archive Fever: A Freudian Impression (1995), Jacques Derrida redefines the archive as a site where memory, power, and law intersect. Far from neutral, the archive operates through inclusion and exclusion—determining what is preserved or erased. Building on Derrida’s insights, this article examines how artists Doris Salcedo, Arkadi Zaides, and Marina Davydova confront the violence of disappearance and the politics of erasure within the context of forced migration and exile. Through close analysis of Salcedo’s Palimpsest (2013–2017), Zaides’ Necropolis (2019), and Davydova’s Museum of Uncounted Voices (2023), the article explores how each work reimagines the archive as an embodied, performative space. Drawing on Heiner Goebbels’ concept of the aesthetics of absence, it argues that these artists construct meaning not through presence, but through erasure, silence, and trace. Human actors are replaced by non-human agents—objects, sounds, and data—that evoke the missing and ungrieved. The article situates these works at the intersection of performance studies, memory theory, and migration studies, showing how performative practices illuminate the spectral dimensions of historical violence. Together, these artists offer a counter-archive—one that resists institutional authority, foregrounds absence as an ethical space, and restores the disappeared to collective memory through acts of performative remembrance.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.054 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".