Testing inhabitant agency in interactive architecture : a user-centered design and research approach
Bibliographic record
Abstract
This thesis assembled, tested and demonstrated an inhabitant-centered approach for research and design in Interactive Architecture (IA). It takes an initial step in addressing a critical gap in the field concerning the lack of empirical evidence to support IA’s fundamental claims, especially regarding inhabitant experience. The approach focused on investigating the question of whether inhabitant experience of interactive architecture (IA), presumably dependent on different models of interaction, could support one of the primordial rationales for the social relevance of IA. The rationale states that IA holds the potential to empower inhabitants in participating in the continuous formation of their environment. However, there is no published evidence to date to corroborate the statement’s validity. In fact, very little research has been done to date on inhabitant experience of interactive spaces in general, hindering our ability to justify its use or to properly ground design decisions. Therefore, this thesis presents an exploratory investigation, set to form the basis for the study of agency and empowerment in IA, and aiming to demonstrate an approach to tackle the problem of user-centered design and research in the field. An extensive literature review is thus conducted, from the concept of agency in the social sciences to an overview of the pertinent literature on interaction. Finally, an approach is demonstrated to generate empirical evidences regarding agency in IA. For that, an IA space is designed (comprehending four different models of interaction), assembled and tested, grounded on two user-centered design studies. The first study was an anticipated experience diary study, where 17 participants reported their imagined daily experience with an IA concept. The second study was a user experience survey where 30 participants inhabited and experienced the assembled IA space. This thesis successfully demonstrated a user centered approach for evaluating interaction in IA design concepts, especially with regard to the possibility of fulfilling one of IA’s many untested claims, making explicit the problems and opportunities observed along the process.
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 | 0.036 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".