Writing and Designing a Book about Design and Designing for Young Designers: People who don’t like to Learn by Reading....and the lessons learned along the way.
Bibliographic record
Abstract
Writing a book for design students based on a range of lessons, maxims and wisdoms that had been accumulated over thirty years in the field seemed like a valid exercise. Unfortunately the task was made initially difficult by the simple realization that designers, who many consider to be visual thinkers, tend not to look to books in their attempts to learn how to design. This realization led, eventually, to the choice of a particular format, though with the added realizations that, while, in the past, we have predominantly looked to traditional formats for the creation of texts, papers and books in Engineering, Design and Science, the developments in our technological capabilities for storing and retrieval of information have, quite recently, changed, perhaps forever, how we choose to access all forms of information. The danger now lies in writing for the sake of recording research activity, and in not writing for effective communication, with due consideration for the implications of the chosen media, the time availability, level of comprehension, and the mode of preferred comprehension of the reader. Tradition is strong in the creation of the written word, but we appear to have passed through what could be described as a ‘tipping point’, which now offers a unique opportunity to encourage and promote more effective modes of communication; more dynamic, interactive, visual, in the appropriate format and location, ‘accurate, brief and clear’, and just when it is needed.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.025 |
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".