Investigating User Estimation of Missing Data in Visual Analysis
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
Missing data is a pervasive issue in real-world analytics, stemming from a multitude of factors (e.g., device malfunctions and network disruptions), making it a ubiquitous challenge in many domains. Misperception of missing data impacts decision-making and causes severe consequences. To mitigate risks from missing data and facilitate proper handling, computing methods (e.g., imputation) have been studied, which often culminate in the visual representation of data for analysts to further check. Yet, the influence of these computed representations on user judgment regarding missing data remains unclear. To study potential influencing factors and their impact on user judgment, we conducted a crowdsourcing study. We controlled 4 factors: the distribution, imputation, and visualization of missing data, and the prior knowledge of data. We compared users’ estimations of missing data with computed imputations under different combinations of these factors. Our results offer useful guidance for visualizing missing data and their imputations, which informs future studies on developing trustworthy computing methods for visual analysis of missing data.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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