Bibliographic record
Abstract
The building and construction industry has been focusing on making more energy-efficient buildings given the high energy and carbon intensity of the sector. Recently, the focus has been increasingly shared with building for occupant health and well-being. The WELL Building Standard (or WELL v2), first launched in 2014 by the International WELL Building Institute (IWBI), is a building certification that aims to directly support and promote occupant health and well-being in buildings. Currently, there are more than 4 billion square feet of WELL projects in 124 countries. In parallel, academic studies are increasingly documenting and evaluating WELL implementations in buildings. However, the current literature is disaggregated and lacks focused review efforts to converge and corroborate conclusions from the growing number of WELL case studies and evaluations. This paper presents a review and bibliometric analysis to enhance the state of understanding of WELL, identify its strengths and weaknesses, and guide future research on the topic. A three-step methodology is proposed, including (i) an article search, screening, and selection using the Scopus database; (ii) a detailed review of the studies applying the WELL Building Standard in actual buildings; and (iii) a bibliometric analysis of the studies (e.g., sources, authors, citations) to map and understand how the field has evolved and where it is heading. The bibliometric analysis work will be implemented using the Biblioshiny (R package) and CiteSpace (Java application) tools. The paper will conclude with concrete recommendations for different stakeholders, such as building designers, owners, researchers, and policymakers.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.188 | 0.089 |
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".