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
PowerPoint, Google Slides, Keynote, Prezi and other visual tools have become ubiquitous in modernclassrooms, business meetings, and engineering briefings. Unfortunately, many slides are poorly designed with a high cognitive load. That is, they either contain too much information or have poorly organized information. Given the inverse correlation between cognitive overload and memory, reducing the cognitive load of slides can lead to more effective presentations – improving communication, retention, and instruction. The paper will first provide an overview of cognitiveload theory and its significance/relation to human factors engineering. Then, selected theories from cognitive psychology, including the expert-novice divide, dualchannel theory, gestalt principles, and constructivism will be introduced. Using authentic examples of classroom slides, this paper will demonstrate how these cognitive theories' practical application can reduce cognitive load. This paper aims to be a "why-to" as well as a "how-to" guide for improving visual pedagogical aids, specifically, slides, in the engineering classroom.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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