Bibliographic record
Abstract
RESEARCH BACKGROUND: In 2012, Tom Kovac - in a group of 100 designers around the world - was invited to participate in the 'TobeUs: 100 people - 100 ideas' project. The brief was for each designer to produce a wooden toy car with clear limitations: using only a 16 x 7.5 x 7.5 cm block of Lebanese cedar wood, and a single cut. 'Benedict' was the eventual design by Kovac. The designs were then exhibited around the world for the next 6 years. RESEARCH CONTRIBUTION: 'Benedict' explores design possibilities at the intersection of architecture and industrial design, which aligns with the goal of TobeUs to bring together industrial designers and architects to work on the same project: to speculate on the development of industrial design in the future. This project extends Kovac's research through architectural and design projects that combine the use of digital tools and manufacturing technologies for the conception and realisation of projects on varying scales, for instance through his ongoing product designs for Alessi. RESEARCH SIGNIFICANCE: 'Benedict' was shown alongside works by other internationally renowned architects and industrial designers including Karim Rashid, Jaime Hayon, Fabio Novembre, Luca Nichetto, the Campana brothers, Harri Koskinen, and Marcel Wanders. 'Benedict' went into commercial production and is available for purchase online and shops around the world. The broader TobeUs project was created by designer Matteo Ragni, and the exhibitions were curated by different curators at each outlet. First shown in 2012 at the Museum of Science and Technology in Milan, the exhibition has travelled to Turin, Bolzano, Venice, Milan (Design Week 2013, 2015 and 2016), Toronto (Design Exchange - Canada's National Design Museum, 2013), Brooklyn (WantedDesign 2014), Berlin, and Moscow (Polytechnic Museum, 2013).
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.023 |
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; both teacher heads agree on what is shown here.
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".