Better by Design? Capturing the Role of Design in Innovation
Bibliographic record
Abstract
The essence of innovation is the process of bringing to market new products or processes which, if successful, generate new economic value. Traditionally, we have come to view this process as one in which the primary inputs are scientific, technological, or commercial. Scientists working in university, corporate or public labs generate new knowledge in a variety of forms that may lead to commercializable outputs. Institutions of higher learning produce highly qualified personnel who transmit knowledge in embodied form throughout the economy, enhancing the innovative capacity of firms. Engineers, technical workers and organizational specialists develop new production processes and improvements to existing processes. Interaction with customers and suppliers provides important knowledge inputs that further contribute to the innovation process. Despite the increasing sophistication in our ability to conceive of and measure innovative activity – inputs, interaction, and outputs – the traditional approach fails to capture an important dimension of the innovation process that leads to the creation of economic value: design. This paper reviews the accumulated evidence – both quantitative and qualitative – that documents the growing importance of design as a key input in the innovation process and as a source of value added in a wide range of sectors. We review a set of conceptual arguments that help us understand the more fundamental transformation underlying these recent empirical trends. Included here is a set of recent literatures on the ‘cultural economy’ and creativity, as well as a related literature on the ‘business of design’. We demonstrate that design employment is growing more quickly than the labour force as a whole, and document how the design workforce is becoming more widespread in a wide range of sectors right across the economy, although its geographical distribution is strongly concentrated in larger metropolitan areas. The rise of design as a source of innovative content and economic value also poses significant challenges for the measurement and statistical documentation of innovative activity. We enumerate these challenges and suggest some strategies for modifying existing innovation surveys to capture the role of design in the innovation process. Our analysis is informed by a recent study of employment dynamics, contracting relationships, freelance activity, and longitudinal mobility in industrial and graphic design in the Toronto economy.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.031 |
| Scholarly communication | 0.016 | 0.033 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".