Bibliographic record
Abstract
This chapter analyses the role of transnational communities (diasporas) in the process of collecting, securing and converting episodes of national trauma into the global visual products of remembering and co-optation. Informed by memory studies and diaspora research, the study explores initiatives taken by international communities to recreate the collective narrative of trauma and change its status in the existing model of mnemonic positionality. Over the 20th and 21st centuries, the Ukrainian Canadian diaspora has become a remarkable mnemonic actor, known for championing very specific ‘symbolic’ causes that aim at generating certain images of the community for a general audience. Among them are recognition and information campaigns about the Holodomor genocide and the creation of multiple cinematic products, which reach out to Canadian and international audiences. The diaspora’s participation in the exhibit for The Canadian Museum for Human Rights was another symbolic, yet controversial, milestone for the diaspora group. Both campaigns are interconnected and have become known for their ambivalent effect on actors from the diaspora and the process of memory politicization across Canada. The research unpacks the diaspora group’s capability as a key mnemonic player amongst other competing stakeholders. The above empirical examples are investigated to show how memory politics within diaspora communities is being instrumentalized.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.206 | 0.042 |
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".