Bibliographic record
Abstract
<JATS1:p>Learning by doing is the best way to get to grips with new ideas, and graphic design is no different. Weaving together creative strategies and design principles with step-by-step Adobe software guidance, this unique book helps you to immediately put into practice the concepts as you’re learning them so they become second nature.</JATS1:p> <JATS1:p>Covering all the introductory topics a designer needs to know – from working with colour and layout, to editing images and designing apps – this fully updated edition of the hugely popular Graphic Design Essentials includes plenty of hands-on instruction and real-life examples to give you a thorough grounding in the fundamentals.</JATS1:p> <JATS1:p>This new edition includes:</JATS1:p> <JATS1:p>- Coverage of AdobeIllustrator, Photoshop, and InDesign</JATS1:p> <JATS1:p>- Examples of designs from the UK, US, Canada, Europe, Hong Kong, China, the Middle East, and Australia</JATS1:p> <JATS1:p>- Smaller supporting activities alongside major project exercises</JATS1:p> <JATS1:p>- New design formats, including apps and infographics</JATS1:p> <JATS1:p>- Downloadable resources to use within the software instruction</JATS1:p>
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.546 | 0.396 |
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".