Memory and migration : multidisciplinary approaches to memory studies
Bibliographic record
Abstract
Table of Contents Introduction Julia Creet:The Migration of Memory and Memories of Migration 1 Section I: The Melancholy of No Return Zofia Rosinzka:Emigratory Experience: The Melancholy of No Return30Srdja Pavlovic:Memory for Breakfast48Veronika Zangl:Remigration and Lost Time: Resuming Life After the Holocaust60Chowra Makaremi:The Waiting Zone81 Section II: Collective Memory Ghettos Andreas Kitzmann:Frames of Memory: WWII German Expellees in Canada111John Sundholm:The Cultural Trauma Process, or the Ethics and Mobility of Memory147Laurenn Guyot:Locked in a Memory Ghetto: a Case Study of a Kurdish Community in France 167 Nergis Canefe:Home in Exile: Politics of Refugeehood in Canadian Muslim Diaspora196 Section III: The Smell of Flowers and Rotting Potatoes Mona Lindqvist:The Flower Girl: a Case Study in Sense Memory230Amira Bojadzija-Dan:Reading Sensation: Memory and Movement in Charlotte Delbo's Auschwitz and After244Marlene Goldman:Memory, Diaspora, Hysteria: Margaret Atwood's Alias Grace265 Section IV: Architectures of Memory Tomasz Mazur:Value of Memory - Memory of Value: A Mnemonic Interpretation of Socrates' Ethical Intellectualism294Luiza Nader:Migratory subjects: Memory work in Krzysztof Wodiczko's projections and instruments313Yvonne Singer:The Veiled Room330Julia Creet:The Archive as Temporary Abode354Bibliography379
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 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".