Design spaces and creativity: playing with LEGO® bricks
Bibliographic record
Abstract
This workshop pack is intended to be used as an open educational resource by educators in research in engineering, design, creativity and related areas. The workshop was developed as part of a PhD research project on design spaces, design creativity and computer games carried out by Esdras Paravizo under the supervision of Prof. Nathan Crilly at the University of Cambridge, UK. The workshop was first run at the Design Computing and Cognition conference (DCC’24) in Montreal, Canada in July 2024. The workshop pack contains the following materials: - Workshop presentation (with an introduction of the topic, overview of the task and discussion slides provided as a .pptx file); - Intructors guide (with detailed instructions for educators that want to host the workshop); - Task template (that participants will use during the workshop). Besides the provided materials, the workshop requires educators to have certain physical materials (LEGO bricks, precision scale, rulers and sticky notes). The files include the English and the Portuguese versions, both of which you can download as separate ZIP folders. All of the materials are provided as-is, under a Creative Commons license CC-BY
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 0.008 |
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".