Plain Language and Ethical Action: A Dialogic Approach to Technical Content in the Twenty-First Century
Bibliographic record
Abstract
Plain Language and Ethical Action examines and evaluates principles and practices of plain language that technical content producers can apply to meet their audiences' needs in an ethical way. Applying the BUROC framework (Bureaucratic, Unfamiliar, Rights-Oriented, and Critical) to identify situations in which audiences will benefit from plain language, this work offers in-depth profiles to show how six organizations produce effective plain-language content. The profiles show plain-language projects done by organizations ranging from grassroots volunteers on a shoe-string budget to small nonprofits to consultants completing significant federal contacts. End-of-chapter questions and exercises provide tools for students and practitioners to reflect on and apply insights from the book. Reflecting global commitments to plain language, this volume includes a case study of a European group based in Sweden along with results from interviews with plain-language experts around the world, including Canada, England, South Africa, Portugal, Australia, and New Zealand. This work is intended for use in courses in information design, technical and professional communication, health communication, and other areas producing plain-language communication. It is also a crucial resource for practitioners developing plain-language technical content and content strategists in a variety of fields, including health literacy, technical communication, and information design.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.076 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".