Bibliographic record
Abstract
Mixity (Mixité): social diversity (ReversoDictionary)... culturally speaking: of or relating to artistic or social pursuits or events considered to be valuable or enlightened (Collins Dictionary). As the initial brunt of the ongoing pandemic comes to a close, we are left with a world that will have changed drastically. Public spaces, and the ways in which we collectively interact with them and one another alike, will need to be redefined. Harmoniously balancing form, function and value across stakeholders is a necessity in the public environment, and investigating outcomes in the shifting nature of these spaces has always been the quest of both their maker and their user. Montréal-based designers Atelier Daily tous les jours have had widespread success in creating experiential spaces of play offering unique interactions for the individual. Continually striving to learn from every project, these designers of public spaces must have one eye on critical past outcomes, and one eye on how the future can and will force them to unlearn— an even more pressing issue in the context of the pandemic. In the following interview excerpts, Daily tous les jours' director, Mouna Andraos, offers a glimpse into the core tenets of the studio, the realities of dealing with cities as clients and how important it is to identify the parts of our practice that are essential to our making.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.271 | 0.105 |
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".