Toxic landscape: Excavating a polluted world
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Studies of the heritage industry, museology and archaeology and nationalism have highlighted vital ways in which the objects archaeologists study—far from being inert representations of the past—are lively, political, and potent in the present. This paper proposes that in order to investigate the long-term impacts of humans on the environment we as archaeologists must extend this reflexive turn to questions of ecological harm and pollution. First, archaeologists need to approach forms of human-derived pollutants as a type of artifact to attend to both the conditions of their production as well as the social effects of ecological degradation in the past. Second, archaeologists need to investigate the ongoing nature of this ecological degradation and its effects in the present. Drawing from my excavations of an early twentieth-century industrial site in Western Canada, I investigate how the rise of industrial-scale production in Edmonton, Alberta, remade the urban landscape by providing new consumer goods and manufacturing jobs, as well as—due to rampant pollution—remaking the environment and the ways in which the local population interacted with it. At the same time, I outline how the remains of this industry impacts the present as a form of pollution that affects local water quality and soil chemistry. Through these effects, industrial artifacts continue to actively transform the ecological relationships of humans and non-humans alike. In so doing, this project demonstrates the value of archaeology as a discipline whose focus on long temporalities and materiality provide unique insight into one of the most pressing contemporary political issues, ecological devastation and its social impact.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it