Bibliographic record
Abstract
Green building design is a socio-technical process, so it is important to engage end users (e.g. building occupants) in requirements gathering. Given that a majority of software tools used in designing green buildings are aimed at engineers, we must determine the most effective way of communicating information about energy use requirements to end users, who are typically unfamiliar with energy analysis techniques. The green building design community is at an early stage in considering how to include end users in the design process. Research has not yet determined how best to present environmental impact information to end users in order to engage them in the process. Research in green building design can benefit from the lessons learned from Requirements Engineering in the software community. This paper outlines an intended research methodology and a literature review. The research will involve an exploratory case study of a Toronto-based green home renovation company that focuses on a holistic building approach. By following clients through their renovation projects, we hope to explore two research questions: why homeowners renovate their homes, and how the industry currently presents information to end users about green home renovation and green home requirements.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".