‘Language in the United States’: An innovative learner-centered, asynchronous general-education course in linguistics
Bibliographic record
Abstract
LIN 200 ‘Language in the United States’ is a large general-education course dealing with linguistic diversity in the United States. It is taught online in an asynchronous format and attracts hundreds of students each semester. The pedagogical innovations adopted in this course include the use of guest lectures by leading experts in the field, the design of discussion board activities to facilitate interaction among students and with instructors, and the organization of the material into adaptable learning modules. We adopt a learner-centered approach using the backward-design framework and applying the community-of-inquiry model. The result is a course that succeeds in achieving its main learning goals: to introduce students to the vast linguistic diversity in the United States and to the basic principles of linguistics, in particular, that human language is primarily spoken or signed (not written), that every human group has its own language, and that all languages are equally capable of expressing any human thought or emotion, although their social prestige may differ.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".