Re-storying Dammed Waters: Towards Kichisippi Pimisi (American Eel) Recovery in Algonquin Provincial Park
Bibliographic record
Abstract
Since time immemorial, the migrations of Pimisi (American Eel, Anguilla rostrata) to the Kichisippi (Ottawa River) Watershed have woven together a vast web of interdependencies. Dam operations along these waters have driven Pimisi to endangerment, impacting ecological balances, cultural ties for the Algonquin Anishinaabeg, and relational understandings of the watershed. \n \nRe-storying Dammed Waters considers the future of Pimisi recovery efforts by intervening in barriers to their habitat in what is now Algonquin Provincial Park in Ontario. Though celebrated for its vast offering of ‘wilderness’ experiences that foster human connection and care towards more-than-human beings, the park upholds colonial and resource-oriented legacies of land management and use. Successive and prolonged dam operations stemming from the park’s logging era to the rise of water management for recreation and hydropower development have resulted in aquatic ecosystem disruptions and biodiversity concerns that are challenging to negotiate. This thesis asks how the design of recovery interventions might reconcile human \nrelationships with Pimisi and other more-than-human beings and systems. \n \nA research process consisting of fieldwork documenting the park and its dams, conversations with allied voices in fisheries management, and case studies of dam intervention approaches reflect upon the planning and implementation of Pimisi recovery in conjunction with its ecological and cultural narratives. The synthesis of these studies imagines an alternative story for the park’s aging Cache Lake dam in support of recovery. Restorative and interpretive interventions within a phased design scheme reinstate the rights of Pimisi to access these waters, improve habitat conditions, and usher in human awareness and care. \n \nBy foregrounding more-than-human lives like Pimisi in a research process attuned to relationality, this thesis suggests that there is potential for an agential and ethical shift in how designers engage with the land. Amidst an ongoing global loss of biodiversity and entwined discourse on reconciliation in architecture, it offers actionable considerations for design that seek to bridge species, scales, and ways of knowing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".