Office to Residential Conversions in Ottawa, Canada : Exploring the Relationship Between Discourse and Policy
Bibliographic record
Abstract
Office to residential conversions have been catapulted to the forefront of urban development as an innovative strategy to combat the decline of traditional urban centres in the wake of the COVID-19 pandemic. This thesis has explored the relationship between discourse and policy, in the chosen case location of Ottawa, Canada, a city that has felt the acute effects of changing in-person work environments since the onset of the COVID-19 pandemic. This has been done by employing various methods including Critical Discourse Analysis, Interviews and Document Analysis while overlaying the findings with Carol Bacchi’s critical framework for analysing policy discourse, ‘What is the Problem Represented to Be (WPR). The main findings are that office to residential conversions have been significantly problematised in municipal planning documents, real estate and organisational reports as well as developer discourses. The findings reveal that the existing policy measures serve largely to facilitate a neoliberal planning model that prioritises private market friendly principles, rather than appropriately considering the needs of its residents over these private interests. This form of policy implementation positions the city as facilitators of growth, developers as skilled problem solvers, and residents as the passive recipients of various intended benefits, despite these benefits being largely unattained, while the city relinquishes full responsibility for policy success, instead tying its failures with current economic realities. Overall, these findings encourage further policy development to more accurately address market and societal challenges while facilitating further urban growth in a manner that properly addresses its current shortcomings.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.042 | 0.027 |
| Scholarly communication | 0.015 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".