Deliberation and Diplomacy: Statue Removals in Two Municipalities in Canada
Bibliographic record
Abstract
In what seems to be a nascent culture of accountability, advocates across Canada, and beyond, are rallying against commemorative symbols, namely statues, to demand systemic change. Increasingly, conversations about removing statues are not just focused on outcomes, whether or not statues should stay or go, but about process – who and in what ways communities should be included within decision making. Democratic deliberation is often imagined as the most fair and just approach to resolving conflict together: the principles of inclusion, equality, and publicity ostensibly ensure that all who wish to share their opinions are heard. However, there are scholars who challenge these assumptions by focusing on the ways that historical injustice has caused structural, procedural, and behavioural discrimination that impacts whose opinions are shared or valued. Thus, contemporary scholars are interested in how deliberation can be modified, particularly within an age of reconciliation, to rectify these inequitable barriers. Because of where statues are situated, within municipal boundaries, it is local governments in Canada that are faced with addressing these complex questions. This research analyzes two distinct case studies of statue removals, the removal of the Edward Cornwallis monument in Halifax, Nova Scotia, and the removal of the John A. Macdonald monument in Victoria, British Columbia, to understand the impacts of each distinct deliberative processes. Considering the Truth and Reconciliation Calls to Action and the principle of self-determination, this thesis shows that deliberating with Indigenous nations and representatives demands a new approach to deliberation, one that I call diplomatic deliberation.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.064 | 0.016 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| 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".