Bibliographic record
Abstract
This thesis examines public engagement with three historical burying grounds in downtown Halifax, Nova Scotia: The Old Burying Ground, the Poor House burying ground, and St. Peter's Cemetery.The research question was inspired by a significant event in 1958 where the Downtown Merchants Association proposed to turn the Old Burying Ground into a parking lot (McGuire 1990).This was met by serious opposition from the public, even with the promise by the Merchants Association to provide a suitable monument on the site to acknowledge its history and significance.This is important because it does not appear that the other cemeteries received the same opposition as they underwent transformations in the twentieth century, such as the construction of a library and the paving of a parking lot.Through the use of newspaper articles from the nineteenth and twentieth centuries, modern news coverage, social media comments and news article comments, I provide a timeline of events related to the sites.Through this timeline, I explore notable themes and similarities of how people have expressed opinions and interests in the burying grounds from the nineteenth century to present.The results of this research suggests that the reasons for maintaining and memorializing the Old Burying Ground in comparison to its neighbours is related to the level of preservation the site holds and its military associations, but also the larger ideas of power that dominate the site.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".