Finding Voices: Bringing the Archive into History Classrooms
Bibliographic record
Abstract
Finding Voices explores the relationship between archives, technology use, and history classrooms and textbooks. Through recent studies and projects, history textbooks have continuously omitted racialized and marginalized histories in Canadian education and often privilege settler-colonial perspectives. This sidelining creates an erasure that negatively impacts students’ capacity for historical analysis. This research aims to shift this paradigm through augmented reality, archival research, and curation. The research questions include: How might engaging with history through artefacts open conversations, inquiries, and curiosity on social justice issues? How might the use of augmented reality combined with pedagogy transform Canadian history learning? How can the method of a/r/tography support the Ontario Ministry of Education’s learning expectations and outcomes while introducing difficult knowledge (Pitt & Britzman, 2003)? What are the pedagogical and creative approaches that teachers and curriculum developers can use to help students learn about, and learn from (Pitt & Britzman, 2003) silenced histories within the classroom? This research was done in four stages: 1) finding archival materials from various archival institutions; 2) creating an app prototype using website-based AR and creating an archive box with the archival materials collected; 3) recruitment of BEd Teacher Candidates; and 4) collecting user experience data across multiple contexts (observation notes/conversations, multiple surveys, workshops, and participatory collaboration in the form of an exhibition). Findings conclude that the participants found this method of learning history engaging and inspired them to consider ways archived-engaged AR pedagogies could be utilized in their own practice.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".