Bibliographic record
Abstract
In contemporary North America, death is contained within a network of cemeteries, crematoria and funeral homes. Death-space and its associative funeral rituals are both sacred and abject resulting in marginalization that adversely affects how the living understand their mortality. \n \nOur perception of death influences our place in the world and funeral ritual facilitates our departure from it. In most cities, the funeral home houses this liminal ritual, while also providing the clinical handling and processing of the deceased body. Investigation of the funeral home and its role within the city addresses how architecture can influence cultural views on death. Through the funeral home there is an opportunity to balance the seemingly opposing narratives of the living and the deceased by bringing them together for the funeral. \n \nIn the City of Toronto, the density of its diverse neighbourhoods is not reflected by a proportionate number of local funeral homes. This thesis proposes a non-denominational space for funeral ritual and cremation within the dense St. Lawrence Neighbourhood. The placement of the Funeral Centre satisfies the practical requirements of this growing community, while the adjacency to the St. Lawrence Market juxtaposes the vibrancy of the ordinary and the solemnity of the sacred. This proposal extends into a network for the scattering of ashes throughout the city aiming to reconnect people to the realities of their existence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".