“Niagara as Technology”: Rupturing the Technological for the Wordy Ecologies of Niagara Falls
Bibliographic record
Abstract
My research-creation examines how colonial language and words inspire the logic behind resource extraction, appropriation, and exploitation. Through found poetry—a creative and analytical process of using different (“found”) sources and various methods to critique and view the world—I create a collection of poems responding to Daniel Macfarlane’s Fixing Niagara Falls: Environment, Energy, and Engineers at the World’s Most Famous Waterfall (2020). Macfarlane claims that the “result” of Niagara Falls is a “compromise between scenic beauty and electricity generation” (208). However, I argue that Niagara Falls is not a “compromised” space but a hub of ecosystems coming into being. My poetic techniques emphasize the arbitrariness of colonial practices that classify beings as successful, political, and economic gains or progress. As such, I use various found methods to think with water and Indigenous modes of healing with Niagara Falls. By redacting, cutting, and layering the found words, I create an ethos of confusion, apprehension, unease, and responsibility in order to call into question the colonial logic that defines how settlers position themselves on Indigenous lands and in order to offer the possibility to listen otherwise.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.035 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".