Bibliographic record
Abstract
While only my own name graces the cover, this book would not have been possible without the generous contributions of so many others.The book was developed out of my graduate research at the University of Alberta, and I would like to begin by thanking my supervisor Astrid Ensslin for her insightful feedback, support, and mentorship throughout that writing process, and for her encouragement to pursue publishing the work.I am also grateful to my first reader Arlene Oak for introducing me to many approaches for studying the built environment, and to Geoffrey Rockwell for introducing me to many productive ways of conducting research.Both of you made my transition from architecture into engaging with other disciplines far smoother.Going further back, the questions that drove this book arose during my formative years at Dalhousie University's School of Architecture, and I would like to thank the instructors, staff, and students that contributed to its constructive and convivial atmosphere, particularly my graduate supervisors Christine Macy and Sarah Bonnemaison.Books such as this cannot be completed without rich source material, and I must thank the developers of the video games analyzed here for creating exceptional works that advance the medium.I would also like
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.352 | 0.321 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".