Stories of Suitcases: Young Girls from Auschwitz to Canada
Bibliographic record
Abstract
The Old Brown Suitcase by Lillian Boraks-Nemetz, who survived the Holocaust in Warsaw and Hana’s Suitcase by Karen Levine, have moved the world for the simplicity yet intensity with which they describe this devastating moment in the history of the Jews. These are books which can teach children about the horrific events but they can also offer alternative readings on issues of racism, identity and diaspora. By learning of the cultures, traditions and languages of others, children are encouraged to be observant, receptive and more open-minded, a first step in eradicating stereotypical prejudices and intolerance towards those considered ‘diverse’.Storie di valigie: bambine da Auschwitz al CanadaThe Old Brown Suitcase di Lillian Boraks-Nemetz, sopravvissuta all'Olocausto di Varsavia e Hana's Suitcase di Karen Levine, hanno commosso il mondo per la semplicità e l'intensità con cui descrivono questo momento devastante della storia degli ebrei. Questi sono libri che possono insegnare ai bambini gli eventi orribili, ma possono anche offrire letture alternative su questioni di razzismo, identità e diaspora. Imparando le culture, le tradizioni e le lingue degli altri, i bambini sono incoraggiati ad essere attenti, ricettivi e di mentalità più aperta; un primo passo per sradicare pregiudizi stereotipati e intolleranza verso coloro che sono considerati ‘diversi’.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.051 | 0.012 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".