Of Beaver Dung and Copper Wires: Rethinking Narrative Geographies of the Central Arctic
Bibliographic record
Abstract
NiCHE has archived 15 video presentations from this event A two-day workshop addressing the myth of the "Great White North" as a major theme in contemporary debates about Canadian geography and identity. Discussions placed critical race theorists in dialogue with scholars studying the idea of nature in order to consider how social constructions of race, whiteness and nature are interconnected in creating the Canadian nation. The objectives of the workshop were: to evaluate how the concept of nature has been and continues to be implicated in the co-construction of race and whiteness in Canada to draw scholarly attention to the geographical configurations of racisms in Canada and elsewhere and to initiate interdisciplinary debate on the complex ways that history and geography are implicated in the production of racialized social formations. Citation : Cameron, Emilie. "Of Beaver Dung and Copper Wires: Rethinking Narrative Geographies of the Central Arctic." Rethinking the Great White North. 1 February 2008. Bio : Emilie Cameron is a Doctoral Candidate in the Department of Geography at Queen’s University, Kingston, Ontario. Her research focuses on imaginative geographies of the Canadian Arctic and their intersection with cultural, political, and economic power. She is particularly interested in story and in articulating a ‘critical narrative geography’ of the Arctic. Abstract : This paper intervenes in efforts to 'rethink' the Great White North by considering the materiality of stories structuring racialized imaginative geographies of the Canadian Arctic, and by attempting to story the Arctic differently by attending to different 'things'. It focuses on 'copper stories' and the networks of people, places, and things involved in the exploration, extraction, and manipulation of copper as a way of rethinking hegemonic understandings of the Bloody Falls massacre story.
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.
How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.018 | 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 teacher head, 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".