Bibliographic record
Abstract
My neighborhood has a character which affords me a “sense of place and identity,” a sense produced by my interaction with my surroundings, a sense which makes “a home and a series of houses a neighborhood.” A community’s “sense of place, its character,” can be created by a collection of structures or by a single structure. But, in the end, it just makes us feel good, feel at home.Threats to that character or ambiance make us apprehensive. Threats carried out upset us. Loss of visual harmony or prospect and loss of identifying structures disorient us. There is a loss of concordance, of things - structures, landscape, visual cues - that made us feel good. We seek to protect and preserve the combinations, the ensemble, which gives us a sense of place. That is what this essay will discuss.Part I will discuss and illustrate environs, a term used to describe a historic property’s “associated surroundings and the elements or conditions that serve to characterize a specific place, neighborhood, district or area.” These characteristics spark a desire “to retain and preserve the distinctive character of historic properties’ environs.”Part II will review Louisiana’s desire to preserve the character, the environ, of the French Quarter in New Orleans. Using the term “tout ensemble” shows that the Quarter is not just a collection of individual structures, but an entity in itself that collectively has a spirit, an ambience and character that merits preservation.Part III will then discuss whether an individual structure, the Satterlee House in West Seattle, has a character, an environ, described not just by the structure but also by its setting, a tout ensemble for an individual structure rather than a collective.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".