Digital Archaeology at the Edge: Sharing Heritage Through Archaeogaming on the Lower North Shore
Bibliographic record
Abstract
The Lower North Shore (Eastern Quebec) raises key concerns for local Innu and Settler ‘Coaster’ communities: (i) the loss of oral traditions and collective memories as Elders pass away; (ii) younger generations leaving due to limited professional opportunities; and (iii) the lack of regional follow-up after archaeological fieldwork with the displacement of their heritage to central repositories. In 2021, public photogrammetry and archaeogaming workshops were organized to explore how archaeology could contribute to regional cultural, educational, and economical development. Two interactive environments were developed to demonstrate in concrete terms how digital archaeology and archaeogaming could help showcase the region’s heritage and build local and sustainable skills while also allowing for the collaborative interpretation of data and construction of narratives. Initial interest led to a 2022 phase where we began developing a collaborative digital platform and extracurricular activities to allow students to digitize artefacts and collect their Elders’ stories. It also raised issues regarding whose voices are prioritized, how conflicting narratives are reconciled, and who holds ownership over digital heritage in the capitalist and colonial frameworks Quebecois archaeology operates in.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".