Architectures of counter remembrance: co-constructing memory box autobiographies with second-generation Tamil refugees
Bibliographic record
Abstract
Historical trauma deeply affects second-generation refugees, who carry the emotional scars endured from their families’ past and present struggles. For conflict-fleeing refugees, state-imposed erasure of histories exacerbates trauma. This article presents findings from a Decolonizing PAR (Participatory Action Research) study with second-generation Toronto Tamil refugees, using memory-box autobiography methods to co-create knowledge about historical trauma and community healing. Two key questions were addressed: a) What are their memories and postmemories, growing up amidst the genocide in Sri Lanka? and b) What collective threads of historical trauma and intergenerational healing emerge from their narratives? Through historical image-based narrative analysis, five threads were identified: (1) intergenerational memories of joy as resistance; (2) fragmented, evolving transmission of postmemories; (3) legacies of state violence; (4) community knowledge and activism; (5) disconnect and diaspora guilt. Through constructing architectures of counter remembrance as public narrative, intergenerational refugee communities can use memory to heal and resist erasure.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".