Locations that resist telling: Making space for socioeconomic analysis in memory studies
Bibliographic record
Abstract
This special issue of Memory Studies draws on the nexus between mass violence and socioeconomic inequality. It emerged from an interdisciplinary workshop entitled “Memory at the Intersection of Mass Violence and Socioeconomic Inequality,” held at Saint Paul University in Ottawa, Canada, in 2018. The special issue assembles a diversity of interdisciplinary scholarship addressing various ways in which entrenched structural violence intersects with memories of atrocities. Contributors examine how economic precariousness, poverty, and marginalization based on class impede efforts toward remembering, reconciliation, and recovery from historic injustices. The field of Memory Studies has conventionally been interested in questions of identity, trauma, and the ethics of remembrance, while neglecting class and socioeconomic concerns. This issue interrogates the enduring material impacts of violence, such as poverty and inequality, and their influence on who is deemed “grievable” and whose pain is acknowledged or overlooked in collective memory practices. Drawing on Crenshaw’s intersectionality, the contributors show how identity and class intersect in differential positioning vis-à-vis violence and its memorialization, while also highlighting the silencing of marginalized voices in communities. The articles demonstrate that physical violence often transitions into structural violence, perpetuating harm long after conflicts ostensibly end. From indigenous women in Canada and Peru to South Africa’s “born free” generation and working-class neighborhoods in Baltimore and Belfast, the issue underscores the necessity of integrating socioeconomic analysis into memory frameworks. By addressing these dynamics, the issue advances a more inclusive and equitable approach to Memory Studies, offering a nuanced understanding of how societies remember and forget amid ongoing inequalities. In so doing, it communicates important knowledge about the ethical and practical dimensions of memory work in contexts of mass violence and structural inequality, thus feeding into academic debate and concrete social justice efforts.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".