Balconies as adaptable spaces in apartment housing
Bibliographic record
Abstract
New requirements for living, working, and learning at home due to Covid-19 have highlighted two fundamental needs in apartment housing: (1) adaptability to fit multiple functions in a limited area; and (2) access to private outdoor space to support residents’ health and wellbeing, and to provide spatial and thermal variety in small units. The two needs may initially appear to be disconnected: when residents have a high demand for flexibility and adaptability in apartment housing, balconies tend to be overlooked as potential spaces to facilitate adaptability. An analysis of several international housing projects with innovative balcony designs and unit designs is the basis for the identification of several typologies of balconies. Typologies of adaptable balconies and examples are used to show how they may support housing adaptability within a dwelling. The ‘adaptable balcony’ concept is introduced in the context of multifamily housing design, together with a clear definition of active and passive adaptability by inhabitants. Practice relevance Apartment balconies are often overlooked as design elements capable of influencing housing adaptability. This paper explores how adaptable balconies could support and improve residents’ functional use of their dwellings. The ease of adaptability, how and to what degree residents can adapt their balcony spaces, are shown in built examples. The ‘adaptable balcony’ concept in the context of multifamily housing can provide developers, designers, and inhabitants with an enhanced, more flexible use of domestic spaces. Several typologies of adaptable balconies are identified and considered for how they may support housing adaptability within a dwelling. Two notions of passive and active adaptability in balcony design can help designers facilitate the desired levels of adaptability in a project.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".