DBDB: A DATABASE FOR DESIGN BIBLIOGRAPHY
Bibliographic record
Abstract
Given the interdisciplinarity of engineering design, and the need of a swift access to an up-to-date bibliography on the subject, the production of a design database becomes an imperative and challenging task. This is how two designers of the McGill NSERC Chair in Design Engineering teamed up with two expert librarians, also of McGill University, in an attempt to produce a database with a search engine that caters to designers at large, with special emphasis on engineers. The Design Bibliography Database aims at helping engineering designers, and designers at large to some extent, find bibliography items on specific topics of their multidiscipline. The first task to face is how to order the extremely rich literature on the subject. The bibliography database is currently being developed under the DBDB Project, as reported here.
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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.046 | 0.058 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.093 | 0.100 |
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".