Building an Engaged University: The River Building as a Spatial Reflection of Carleton University’s Social Mission
Bibliographic record
Abstract
This thesis explores the use of a multifaceted approach to space to identify the similarities and differences between the rhetoric of engagement within the university and the practices on the ground.This research emerges from tensions between shifts in the structure and purpose of the university as a social institution and a growing commitment to engagement evident in the rhetoric of the university.This thesis analyzes Carleton University's proposed commitment to engagement, as presented in its strategic and academic plans, and the conceptions of engagement that are reflected in and supported by the design, mandate, and administration of the River Building.This thesis concludes that only studying the rhetoric of the university does not present an accurate picture of the university and that space can be used to further identify the similarities and differences between this rhetoric and the practices and policies implemented in a particular space of the university.First, I must acknowledge my co-supervisors, Dr. Peter Andree and Dr. Rebecca Schein, for their guidance, support, and patience throughout this process.Both Rebecca and Peter offered countless suggestions and challenged me in my analysis in a way that has made me both a better researcher and writer.I would also like to thank them for providing a space in which I was comfortable to work through my ideas, no matter how underdeveloped, and be my quirky self.I
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.049 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".