Value-based Characterization of Cultural Landscapes: \nThe Cases of Villa Thompson and Villa Sparrow, Mont-Royal, Montreal
Bibliographic record
Abstract
This thesis is a cultural study of the cultural landscape of Villa Thompson and Villa Sparrow located on Mont Royal, a Natural and Historic District in Montreal, Canada. Mont Royal has in time gained a high level of notoriety and authority for informing the cultural history of Montreal, Quebec and Canada, and this site is read using value-based theory and methodology, a practice first applied to historic buildings and monuments analysis in the early 20th century. In response to the expansion of the notion of heritage and the need for better tools to analyze and understand historic territories, “characterization” as field of inquiry and methodology was put forward in the early 1990s, offering new ideas for understanding cultural landscapes. This study argues that a value-based characterization provides a useful understanding of this territory, and one which contributes to a better understanding of all territories through a more lucid knowledge-based cultural landscapes strategy. Defining a secure knowledge-base thusly paves the way to bring cultural landscapes into meaningful recognition and conservation. Mont Royal, including the Thompson and Sparrow Villas, has been recognized as a National Historic Site by the Government of Canada, a Natural and Historic District, Arrondissment Historique, by the Government of Quebec and as a Heritage Site by the City of Montreal. Furthermore, the cultural landscape of the Thompson and Sparrow Villas is recognized and designated as Historic Site, or Site Historique, by the Government of Quebec. These recognitions are, by and large, based on the symbolic and emblematic values placed on Mont Royal and these buildings, and Montrealers' sentimental attachments to and appropriation of the space, as well as the urgent need for its protection. This study argues that the site of Thompson and Sparrow Villas, as a cultural landscape, is a valid cultural document, and therefore requires understanding through cultural study within the field of inquiry and methodology of characterization. From its role as a crossroads for aboriginal peoples for thousands of years, to its first European occupation by French settlers, to its exploitation as agricultural land, to its appropriation for leisure, and finally to its occupation by English merchants and their luxurious villas, this cultural landscape and Mont Royal as a whole are considered worthy texts for reading, analyzing and understanding the cultural values, world views and history of their successive occupants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".