Bibliographic record
Abstract
Purpose – As design becomes more important for business, designers and business people need to work together more. However, they tend to find the relationship difficult, challenging and less productive than either side would wish. The purpose of this paper is to help both designers and business people work more productively with one another. Design/methodology/approach – The paper identifies the underlying schism between validity, which is favored by designers, and reliability, which is favored by business people, as the source of the relationship conflict. It then uses the key attributes of validity and reliability to form recommendations for each side to deal better with their counterparts. Findings – There are five practical and actionable things that designers can do to work better with business people and five equivalent things that business people can do to work better with designers. Originality/value – Currently, neither business people nor designers have a productive or coherent theory as to why their counterparts behave in ways that appear to them to be unproductive. To fill the theory gap, they tend to think badly of their counterparts. This paper provides both sides a productive theory of the other and a prescription for utilizing the theory to promote more productive collaboration.
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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.029 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".