Bibliographic record
Abstract
Architecture, as it exists today, is deeply rooted in perceptions that were established during the Renaissance, which credited the architect as the sole author of creative thinking processes and the resultant design ideas. Since then, the architectural profession has desired to develop new and innovative ways of building, often without being bound by traditions, the environment, or any other constraints and limitations. This approach has frequently failed to address the needs and concerns of many. As a result, architects have not been successful in imparting significant social change that is valuable to large portions of the population. In contrast, however, many other industries have adopted shared design and production practices for the benefit of the masses, warranting further exploration into how architectural practice might evolve its current modes of operation. Wood as a building material has many beneficial characteristics–specifically its widespread availability, versatility, and ease of workability–which make it particularly suitable for investigating shared authorship and collective production methodologies. As an alternative to steel and concrete for mid-rise and high-rise buildings, mass timber construction, in particular, has experienced significant advancements in recent years, resulting in the development of entirely new building processes that rely on innovative engineered wood products, digital manufacturing, and prefabrication techniques. However, this has frequently led to expensive one-off proprietary solutions that are limited in their application. To foster innovation and disseminate knowledge, an open source culture of designing and sharing is necessary. To this end, this paper will present approaches for open source mass timber construction systems that can be applied to a wide range of scenarios and settings, with the aim of ultimately increasing the acceptance and market share of wood construction for the benefit of society at large.
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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.022 |
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".