image2data: An R package to turn images in data sets
Bibliographic record
Abstract
Data visualization is an essential and powerful tool to generate hypotheses, uncover patterns, and disseminate findings. It is crucial that introductory statistics courses train students to become critical authors and consumers of data visualization. Ludic data sets might help teaching statistics to students by making graphics more enjoyable, using images as instantaneous feedback, encouraging to discover hidden patterns, and reducing their focus on traditional hypothesis testing. Those data sets that have hidden images can be difficult to come by for teachers. These considerations have led to the development of a package that could easily create data sets from images for educational purposes. The purpose of this study is to present image2data, an R package that generates data sets from images. In this study, we show how to install the package, explain the basic arguments, and show three examples on how it can be used for teaching. Future studies could evaluate the effectiveness and motivation generated by using hidden images in data sets. Our hope is that by using hidden image in data sets, students will be inspired to decrypt data sets and discover the unexpected.
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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 | 0.008 | 0.057 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.116 | 0.055 |
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".