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
Objective: Given the current concern across the United States with improving community-college student outcomes, particularly in developmental education, understanding what students encounter inside developmental education classrooms is a necessary first step. Method: Drawing on data from a study of teaching practices inside developmental math courses at two large, urban-serving community colleges in the Northeast United States, I open up the “black box” of developmental math teaching at the community-college level. Focusing specifically on data gathered through classroom observations, instructor interviews, and curricular artifacts from six sections of developmental math, I explore two distinct curricula as they were enacted in class sessions and through the classroom discourse around solving math problems and analyze the extent to which each approach reflects the recommendations for mathematics instruction advocated by professional mathematics associations. Results: I found that differences in pedagogical goals (and related notions of mathematical proficiency) were integrally linked to differences in the what and how of assessing student learning, and that contrasting approaches to assessment maintain critical implications for accounting for failure inside developmental math classrooms. Contributions: I conclude with insights regarding future research and reform, for developmental math instruction both to realize robust mathematical learning goals and to facilitate students’ successful completion of developmental math courses.
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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