IN-BETWEEN SPACES: ATMOSPHERES, MOVEMENT AND NEW NARRATIVES FOR THE CITY
Bibliographic record
Abstract
We often think of architecture as distinct buildings, yet as we move through the city we continuously pass through a built environment that is a collage of buildings. These spaces between buildings are underestimated as influences on our experience of everyday life in the city. Considering architecture as linked existential experiences through spaces rather than confined to individual buildings is more in line with our experience of the city as a series of interconnected spaces and places. Rather than describing a single, static architecture through words, how can we express this linked experience of spaces dynamically through narratives? Can writing reveal subtle experiences of interconnected atmospheres within the city? If so, how could this understanding inform the way architects consider the relationship between built-forms and the greater urban context? This thesis investigates and interprets the atmospheres of pathways and in-between spaces to understand their influence on the experience of moving through the city. Although in-between spaces are where much of the human experience of the city occurs, they are rarely discussed and, in many ways, this keeps them out of the realm of design consideration. Atmospheres (discussed by Peter Zumthor, Juhani Pallasmaa and Alberto Pérez-Gómez) arise from our multi-sensory perception of space. The thesis investigates the literature surrounding the phenomenological study and design of atmospheres as well as the connection between atmospheres and narratives—how they develop and are essential in creating meaningful spaces for embodied-experience. Assuming the identity of the flâneur—city-wanderer, documentarian, and observer—as an entry point into investigating the city through movement, the project aims at documenting and rendering atmospheres in the city through photography and writing. Through the intersection of architecture, atmosphere and narrative, the thesis presents a multi-media study of atmospheres of in-between spaces in Montreal focusing on writing as a tool for generating new narratives that inform design. This research project develops a new methodology that reorients design-thinking towards a sequence of experiences through atmospheres in the city. Ultimately, this research sets the conditions, methodology and context for the architectural intervention of attuned public spaces for events along an extension of Parc linéaire du Réseau-Vert (the Greenline).
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.040 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".