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
Most school-age boys score lower than girls at every level on standardized tests of reading comprehension in almost every country where tested. The amount of reading that a child does is directly related to reading fluency; the more one reads, the more proficient one becomes. After reviewing theories and research studies investigating why boys perform less well than girls, a consensus emerges that one reason boys read less is because the kind of reading they are given to do in school does not connect to their interests. A small empirical study in one rural elementary school provides further insight into motivations for reading and non-reading by both boys and girls. The evidence is incontrovertible that as a group, school-age boys score lower than girls at every level on standardized tests of reading comprehension, in almost every country where tested, most notably in the United States (NCES 2002), Canada, England, and Australia, where students are continuously tested. Therefore, the obvious conclusion from this data is that we are failing to make readers of our sons. Analyses of statistics are many and controversial, especially as the latest round of “educational reform” fueled by the Education Act of 2001 has generated more high-stakes testing of students and measurable accountability on the part of teachers, schools, and school districts. Additionally, computers have made gathering, storing, and analyzing statistics simpler than ever before, and the Internet has made it easier to publish and retrieve them. But how do the children themselves feel about reading? Teachers and school library media specialists (SLMSs), trained in reading, in books, and in best practices, often assume that they know what is best for students. At what juncture should the students’ viewpoints be taken into
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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