Repoliticizing memory studies: A concluding roundtable discussion
Bibliographic record
Abstract
This roundtable discussion, featuring an interdisciplinary panel of scholars from the fields of History, Philosophy, Psychology, Peace and Conflict Studies, among others, reflects on what there is to be learned from incorporating socioeconomic analysis into the study of memory, particularly during ongoing violence, or in its aftermath. Panelists discuss how these themes emerge from their work, how they incorporate intersectional, intergenerational and postcolonial analyses, as well as critiques of neoliberal regimes of memory, and what this means for the application of memory politics such as in truth and reconciliation commissions. The panel concludes with reflections on the implications of this discussion for the broader field of Memory Studies, and how the study of memory necessitates contextualized, politically engaged and nuanced analysis.
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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 | 0.041 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.017 |
| Scholarly communication | 0.022 | 0.028 |
| Open science | 0.011 | 0.014 |
| Research integrity | 0.036 | 0.038 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".