Bibliographic record
Abstract
Karen A. Schriver is the author of dynamics in document design: creating texts for readers, an extensive, multidimensional portrait of what readers need from documents and of ways to integrate word and image in order to better meet those needs. She is the former codirector of the graduate program in technical communication and document design at Carnegie Mellon University (Pittsburgh, Pennsylvania). She has been a visiting professor at the University of Utrecht in the Netherlands and at the University of Washington in Seattle. A popular speaker, she has presented her ideas in Japan, the United Kingdom, Canada, and across the United States. Winner of five awards for her research, she now heads her own company, KSA Document Design & Research. She helps organizations improve the quality of their paper and electronic communications through strategies based on research and best practices. She is now working on a book about the nature of expertise in information design. When she is not writing, working with clients, or running to catch a plane, she spends time playing with her two crazy dogs: Cody (a Bearded Collie) and Tika (a little Muttley). She can be contacted via e-mail at schriver@cmu.edu
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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".