Bibliographic record
Abstract
Situating Design in Alberta makes the case that design has the potential to drive economic growth, improve quality of life, and promote sustainability in the province and across the country. Contributors bring both scholarly and practice-based perspectives and come from diverse disciplines including architecture, interior design, industrial design, and visual communications. The collection is organized around four main topics—history, education, business, and sustainability—within which the authors explore a wide range of issues. This synergy of different design approaches lends a sense of forward momentum to the field, stimulates reflection about opportunities and challenges for both practitioners and policy makers, and provides a model for future studies in other regions. Foreword by Douglas J. Cardinal. Contributors: Tim Antoniuk, Ken Bautista, Carlos Fiorentino, Maria Goncharova, Andrea Hirji, Mark Iantkow, Barry Johns, Lyubava Kroll, Courtenay McKay, Skye Oleson-Cormack, Isabel Prochner, Janice Rieger, Elizabeth Schowalter, Megan Strickfaden, Tyler Vreeling, Ron Wickman
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 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".