I Went for a Walk: Observations, Reflections, and Imaginings upon Montreal's Everyday Thresholds
Bibliographic record
Abstract
I went for a walk. Borrowing from the methods of The Situationist Movement and setting out to explore "the in-betweenness" of the city of Montréal, I set out on a series of personal "drifts." These spaces I was looking for are most commonly defined in architectural practice as threshold space. A threshold is a space of anticipation existing at the convergence between different spatial conditions. It possesses such depth that it may elicit a profound stimulation of the senses in a human body. A threshold in the city may be perceived as impressive or innocuous according to the subjective and personal relationship with the inhabitants. Monumental thresholds tend to be remembered for their uniqueness, while everyday thresholds are forgotten for their repetitive nature. Therefore, this thesis attempts to explore the everyday thresholds we use to shape our daily lives. Eventually, this thesis becomes a space to imagine new threshold conditions based on observations and reflections from the drifts.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".