Corrosive Comparisons and the Memory Politics of “Saming”: Threat and Opportunity in the Age of Apology
Bibliographic record
Abstract
This article contributes to the interdisciplinary fields of memory and historical justice studies by analyzing one, particularly troublesome kind of competitive comparison that sometimes happens in memory politics in the so-called age of apology. The article calls this kind of competitive comparison, “saming”. Saming involves the attempt, via far-fetched or otherwise wrongheaded comparison, to exploit the recognition of some well-known case of historical injustice. Further, saming involves pursuing this comparison in ways that both trivialize the original injustice and undermine the framework from which the recognition of that injustice derives. The article develops its arguments and analysis by studying Budapest’s House of Terror museum and two Canadian redress campaigns, which sought historical recognition for the wartime internments of persons of Italian and Ukrainian ancestry, respectively. Saming is a recurrent problem, ubiquitous and probably inevitable in memory politics because the recognition of historical injustice brings with it unavoidable and indeed often valuable incentives to comparison. Thus, the overall aim of this article is to analyze the threat of saming in order to better defend the cause of comparison in introspective collective 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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.059 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".