Translating Encounters with Stone: Investigating Rubbing as an Ecological Method of Inquiry within Architectural Material Studies
Bibliographic record
Abstract
This thesis situates the practice of rubbing within the context of an immediate geological feature in Southern Ontario, the Niagara Escarpment, as a site that is admired for its natural and productive qualities. Adverse to extractive and consumptive attitudes about geological expression, I engage in a discourse that centers nuanced encounters and temporal spectrums at the scale of the hand. Over the span of four seasons, I conduct multiple rubbings along the cliff face informed by multisensorial instincts as observation and inquiry. Allowing my sense of touch and curiosity to guide me, I open myself to an ecological dialogue with the material of stone through listening to the interactive elements. Temperature, humidity, and weather movements are captured within the rubbing process. Traces of flora, human markings, and rock deposits are captured within the paper and resultant rubbing. \n \nI navigate the Niagara Escarpment through memory, exploring rubbing sites through personal landmarks integral to my understanding of the importance of forming interspecies relationships. Adapted from the practice of Chinese rubbings, I choose to experiment with the technical and affective elements of this rubbing process to exercise my observational lens. I explore ideas of placemaking through my intention to reconcile with my heritage and the landscape that is formative to my perspectives on materialism and my approach to spatial expression. \n \nTranslating Encounters with Stone encourages the observation of interelemental exchanges with rock to decentralize acts of human-led practices. In doing so, this act provokes a case for immersive non-human led practices of material engagement. As an approach to reconciling ecological intimacy within a society of stone, rubbing acts to strengthen the environment-human relationship in the natural and built environments we engage.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.049 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| 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".