“Long, Long Time Ago”: Collaborative Engagement with Indigenous Descendant Communities in Ontario through Object Elicitation
Bibliographic record
Abstract
While archaeological objects have meaning for all archaeological stakeholders, this thesis upholds that for descendant communities, archaeological collections embody connections to ancestors across time. Within the heritage field in Ontario, Indigenous descendant community perspectives have historically been omitted from the analysis, interpretation, and dissemination of information about people in the past. Recent efforts to expand training, education, and consultation, alongside a growing number of repatriations of material culture, have begun to address the gap created through the exclusion of descendant communities. In this research, I demonstrate how ethnographic methods can be used to engage across different worldviews, and to share knowledge reciprocally with archaeological stakeholders (who are understood to be archaeologists, academics, descendant communities, and the public), as another way to bring descendant community perspectives into the archaeological process. This thesis presents the design and implementation of three object elicitation workshops held in Northeastern and Eastern Ontario, where descendant community members were asked to examine and discuss archaeological objects excavated from the regions around their home communities. Object elicitation is an interview method centered around objects as a memory trigger within formal and informal discussions. I draw on the conversations held with Indigenous descendant community members to challenge how material culture is understood from a Western archaeological lens, supported by a theoretical and methodological background oriented toward Indigenous and decolonial approaches, which challenge the paradigm of objects-as-specimens. Reframing archaeological objects—toward a view of artifacts as agents of knowledge transmission, as both utility tools and works of art, and as belongings, heirlooms, or ancestors—shifts how material culture and archaeological data are interpreted. By examining the themes which emerged in these workshops, I discuss how collaborative approaches may be used to re-examine understudied and “legacy” (Orchard et al. 2021) archaeological collections in a culturally informed manner.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.015 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".