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
The prospect of speed reading--reading at an increased speed without any loss of comprehension--has undeniable appeal. Speed reading has been an intriguing concept for decades, at least since Evelyn Wood introduced her Reading Dynamics training program in 1959. It has recently increased in popularity, with speed-reading apps and technologies being introduced for smartphones and digital devices. The current article reviews what the scientific community knows about the reading process--a great deal--and discusses the implications of the research findings for potential students of speed-reading training programs or purchasers of speed-reading apps. The research shows that there is a trade-off between speed and accuracy. It is unlikely that readers will be able to double or triple their reading speeds (e.g., from around 250 to 500-750 words per minute) while still being able to understand the text as well as if they read at normal speed. If a thorough understanding of the text is not the reader's goal, then speed reading or skimming the text will allow the reader to get through it faster with moderate comprehension. The way to maintain high comprehension and get through text faster is to practice reading and to become a more skilled language user (e.g., through increased vocabulary). This is because language skill is at the heart of reading speed.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.169 |
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