Bibliographic record
Abstract
Wanting to gain the most out of my unique opportunity to travel to Montreal, I opted to use this adventure as the basis for expanding my own professional development. Living and working in two very distinctive areas of the popular Plateau neighbourhood, the streets offered a particular residential landscape, the likes of which do not exist in France. The main element that distinguishes these urban landscapes from others more familiar, is the notion of frontage ; the residual space between a building Facade and the vehicular lanes. Throughout his book, ‘Reconquérir les rues’, author Nicolas Soulier raises awareness regarding this notion, and extrapolates that it is in fact the residents’ appropriation of this frontage space that most benefits the general ambiance of the street and therefore, of the urban environment at large. There is a sense of idealisation about these interval spaces, that fall somewhere between public and private realm, but how is it actually presented in reality? In order to answer this question, I have developed a methodology based on three key points: Firstly, a systematic analysis of the typologies found within a cross section of the neighbourhood. Secondly, I will conceptualise and design one of these frontage spaces. Finally, I will present my finding with a video performance.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.003 |
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".