Blurring the lines between history education 100 Voices and activism: How 100 Voices remembers the Armenian Genocide
Bibliographic record
Abstract
This article explores how a group of Armenian young adults in Toronto remember the Armenian genocide from afar, 100 years after it happened. The data comes from 100 Voices: Survival, Memory, Justice, a multimedia project that commemorates the 100 year anniversary of the Armenian genocide. Through a detailed analysis of in-depth interviews with the project team, and a thematic and visual analysis of a sample of video clips posted on YouTube, this article claims that 100 Voices blurs the lines between history education and activism. The production team’s use of audio-visual testimony and ensuing visual and discursive strategies open up a space for project participants to address their non-Armenian Canadian peers, teaching them the history of the Armenian genocide. These choices by the production team, on the other hand, enable project participants to articulate the impact of the genocide and its ongoing denial by Turkey through the discourse of human rights.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.019 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".