Extraneous information and graph comprehension
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Purpose – The purpose of this paper is to examine if university students could accurately extract information from graphs presented in 2D or 3D formats with different colour hue variations or solid black and white. Design/methodology/approach – Participants are presented with 2D and 3D bar and pie charts in a PowerPoint presentation and are asked to extract specific information from the displays. A three (question difficulty) × two (graph type) × two (dimension) × two (colour) repeated measures ANOVA is conducted for both accuracy and reaction time. Findings – Overall, 2D graphs led to better comprehension, particularly when complex information was presented. Accuracy was similar for colour and black and white graphs. Practical implications – These results suggest that 2D graphs are preferable to 3D graphs, particularly when the task requires that the reader extract complex information. Originality/value – For the past several decades, diagrams have been valuable additions to textual explanations in textbooks and in the classroom to teach various concepts. With an increase in technological advancements, many authors add extraneous features to their graphs to make them more aesthetically pleasing. This paper has shown, however, that 3D rendering may negatively affect graph comprehension.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it