Probing the use of charts and graphs in technical documentation through analysis and pragmatic collaboration
Bibliographic record
Abstract
A continuous priority for technical writers is to increase user comprehension of concepts and statistical information in technical documentation. Improvements in the written language such as consistent terminology, use of an established writing style, accurate indexing, advanced search capabilities, and so on, address some of the user pain points. The complexities of certain topics do not always translate well into writing; therefore, there is a need for other aids such as information graphics to assist with visualization and to increase the efficacy of information communication [1]. The benefits of information graphics in documentation and learning modules are well documented by the works of Levin and Pane [2, 3]. The conclusion is simple according to Davison, Pane, and Bertrancourt: if graphics are crafted correctly with the intent to enhance understanding then the quality of the documentation will improve and ultimately enrich the user experience and comprehension [4, 5, 6].
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 | 0.055 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".