Bibliographic record
Abstract
This chapter provides an overview of a curricular core called the Sociological Literacy Framework (SLF), a valuable resource for thinking about and improving sociology courses and curricular design, faculty teaching, and student learning. We begin by briefly reviewing the debate about having a core in sociology and then summarize the Measuring College Learning Project, which resulted in the development of the Sociological Literacy Framework. We then frame how and why the ASA Task Force on Undergraduate Education adopted the Sociological Literacy Framework in their national guidelines for the undergraduate sociology major. Next, we examine how the Sociological Literacy Framework is being currently utilized by sociology departments to redesign their curriculum and by scholars in an NSF research grant on curriculum mapping. Because curricular revision and assessment require collective teamwork of faculty within a sociology program or department, and the SLF provides guidelines to enable these department discussions, we conclude with some best practices for sociology programs to consider as they work to improve their curricula.
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How this classification was reachedexpand
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 | 0.033 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".