Above, Beneath, and Within: Collaborative and Community-Driven Archaeological Remote Sensing Research in Canada
Bibliographic record
Abstract
This thesis investigates the application of geophysics and remote sensing techniques in community-driven and collaborative archaeology research in Canada. While these techniques have become common among some archaeologists, they have yet to be extensively used within the lens of Indigenous archaeology. In the introductory chapters, I present the current Canadian context and review the theory, method and application of these techniques to archaeology. I argue for a reconsideration of how these techniques are applied and interpreted within Indigenous contexts, specifically, where these applications have fallen short and how these techniques impact and are shaped by modern Indigenous communities. I propose a methodological approach that incorporates multiple lines of evidence, Indigenous knowledge, and Indigenous archaeology principles, as a potential ‘middle range’ solution. To illustrate how this approach can be applied with Indigenous communities in Canada, I present the methods and results of three community-driven unmarked grave surveys and two collaborative archaeology projects. Drawing on these case studies, I demonstrate 1) that these techniques are effective at contributing to common community-based research goals in a wide range of sites and environments, 2) there are unique factors present when working with Indigenous communities that need to be reflected in and balanced by research designs, 3) the incorporation of multiple lines of evidence and collaborations with Indigenous communities will result in more holistic, meaningful, and co-produced narratives for communities and researchers, and 4) when framed and designed in an engaged and respectful way, archaeological remote sensing can contribute to modern Indigenous communities’ needs and objectives.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.027 | 0.011 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".