Bibliographic record
Abstract
How do we discover a new place and begin to get acquainted with it? In Canadian cities, the sense of place can be difficult to grasp. The relative youth of the built form of our cities and a constant influx of new people from other cities, provinces, and countries continuously re-calibrate what place means. In Calgary, the sense of place includes relationships to its surroundings and the stories that are tied to the city. Ideas surrounding place are essential for architects who want to design while considering context. The question this thesis examines is: How can we learn about place, describe it, and share it, while respecting a multiplicity of experiences and histories of the city? \n \nThe act of mapping is one of the ways in which designers can begin to understand and express a sense of place. This thesis explores the connections between place, memory, and narrative and how mapping can share these aspects of experience. Through mapping, four stories of the city of Calgary emerge from a mixture of personal experience, historical maps, and research. These maps begin to express place through describing official and unofficial histories, experimenting with material and scale, and presenting narratives of the city that come through lived experience in a place.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.055 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".