Encountering the Waterlands: Stories of Environment, Animals and Architecture in the Ahiak
Bibliographic record
Abstract
In spring of 2019, I travelled through Iqaluktuuttiaq (Cambridge Bay), Nunavut to the Ahiak (Queen Maud Gulf) Migratory Bird Sanctuary for a five-week volunteer position studying populations of migratory geese. In this space of migration, I question not only how we understand our changing environment but also how we can recalibrate a relationship in it. In so doing, I approach the Karrak Lake research station as a multiplicity of landscapes, buildings, animals and climatic forces, putting forward a method of engagement and expression that engages each of these actors through photographic composites and narrative-based writing. \n \nThis research is informed by a wide spectrum of cultural study, historical research, the philosophies of Gilles Deleuze, Félix Guattari, Henri Bergson, and James Gibson among others as they helped to reflect upon personal encounter with the Arctic environment over the course of five weeks in the Ahiak. The narratives were composed largely in-situ and tell the story of intense interrelations between living beings, landscape, weather and architecture. The thesis reframes the research station as an integrated component in much larger environmental processes. It explores the interconnectedness of the humans and animals whose territories it sits among, as well as its unique ecological surroundings, and looks toward how we can pursue a relationship with the land in the context of Canada’s changing environmental and reconciliatory discourses.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.022 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| 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".