The archaeology of toxic heritage: novel approaches to understanding contamination and heritage in Labrador, Canada
Bibliographic record
Abstract
This dissertation uses a novel approach to incorporate contamination and pollution as an archaeological class of evidence, in Labrador, Canada. Although contamination from human activity exists across Canada, northern regions hold many of the ~27,000 orphaned or abandoned mines, 63 DEW Line sites, and military infrastructure from WWII. Contemporary archaeological practice is only beginning to grapple with toxic contamination despite the many disciplines, such as geography and history, already confronting this issue. Filling this gap, this dissertation focuses on the archaeology of toxic contamination from military installations in Labrador. Using traditional archaeological modes of analysis, such as mapping, survey, and geochemical and statistical analyses, this research not only shows the extent of contamination in Labrador, but also its everlasting impacts on people, place and heritage. There are three manuscripts that serve as the body of this dissertation, each one addressing a specific aspect of the project. The first manuscript uses mapping and spatial analysis to illustrate the interactions between settlements, heritage sites, contamination and the military in Labrador. The second manuscript explores dendrochemistry applied within archaeology to investigate historical and environmental contamination impacts. The final manuscript conceptualizes contamination as heritage to leverage further archaeological inquiry into the past. Combined, this body of work represents a new approach in archaeology integrating dendrochemistry, spatial analysis, and applications of existing theoretical framing.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.022 | 0.024 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".