Bibliographic record
Abstract
In this article, I argue that the meaning of memorial space can be better understood by turning our focus to the ambient characteristics of these spaces. Ambience is used here to refer to the environmental features that exist at the periphery of our attention. Here, I offer advice from ancient teachers of the art of memory that prescribes spaces with particular environmental qualities as evidence of the important role that these spaces serve in the storage and recollection of memories. Advice such as practice in solitude in dim lit spaces—or well lit, according to the teacher—highlights the importance that these ancient teachers placed on ambience for the proper storage of memories. We also know that these architectural mnemonic features were also “turned outward” to convey meaning. Instead of the traditional semiotic approach to architectural criticism that looks at commemorative symbolism, this article follows an approach that interrogates architecture’s mnemonic function. An analysis of the ambient features emphasized in Ottawa’s urban planning documents reveals that this is an important yet often overlooked aspect of urban spaces by those who study memorials. The Todd Plan, The Holt Report, and The Gréber Plan serve as a foundation for understanding the character of Ottawa’s space. Other more recent urban planning documents from the National Capital Commission are brought in to demonstrate how the role of ambience is afforded even greater prominence than it already was. Reconsidering the different types of spaces (natural and open space) and the role of sensation as uniquely ambient features of space open up new possibilities for examining the memories that these characteristics invoke in audiences. By unpacking the meaning of memorial space, through a mnemonic lens we are able to more thoughtfully engage with the reception of these spaces by visitors.
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 | 0.001 | 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 | 0.005 | 0.032 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".