Bibliographic record
Abstract
The process of designing artifacts is a creative activity. It is proposed that, at the cognitive level, one key to understanding design creativity is to understand the array of symbol systems designers utilize. These symbol systems range from being vague, imprecise, abstract, ambiguous, and indeterminate (like conceptual sketches), to being very precise, concrete, unambiguous, and determinate (like contract documents). The former types of symbol systems support associative processes that facilitate lateral (or divergent) transformations that broaden the problem space, while the latter types of symbol systems support inference processes facilitating vertical (or convergent) transformations that deepen of the problem space. The process of artifact design requires the judicious application of both lateral and vertical transformations. This leads to a dual mechanism model of design problem-solving comprising of an associative engine and an inference engine. It is further claimed that this dual mechanism model is supported by an interesting hemispheric dissociation in human prefrontal cortex. The associative engine and neural structures that support imprecise, ambiguous, abstract, indeterminate representations are lateralized in the right prefrontal cortex, while the inference engine and neural structures that support precise, unambiguous, determinant representations are lateralized in the left prefrontal cortex. At the brain level, successful design of artifacts requires a delicate balance between the two hemispheres of prefrontal cortex.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".