Anchors and ratios to quantify and explain y-axis distortion effects in graphs.
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
, information that can be perceived from a graph, to explain ratings of differences in bar graphs. Study 1 examined whether the upper y-axis truncation effect exists or not. We confirmed its existence even though the effect size is smaller compared to lower y-axis truncation effect. Study 2 examined lower and upper y-axis truncations and expansions. We found that, compared to graphs without distortions, observers perceive larger differences between values when there is truncation and smaller differences when there is expansion at either end of the y-axis. Study 3 examined whether the effects of lower and upper y-axis distortions are also present on reversed bar graphs. We found that the black bars biased observers more when they are truncated, as it reduces their area. Finally, Study 4 examined the impact of y-axis distortions on bar graphs, dot graphs, and line graphs. We found that a plot not showing bars results in less biased judgments in the presence of truncation and similar biases for lower and upper truncation. We discuss the results of other relevant research using these anchors and argue that characterizing graphs using the anchors proposed herein can be generalized to other data visualizations. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it