COURSE TITLE: FINDING COMMON GROUND WITH CONTENTIOUS ISSUES: Educating for Freedom and Responsibility NO OF CREDITS: 2 QUARTER CREDITS WA CLOCK HRS: 20
Bibliographic record
Abstract
COMPLETION DATE: 3 months from your registration date LEARNING ENVIRONMENT: This course requires assignment responses to be posted in a password-secured ONLINE website hosted by The Heritage Institute. COURSE DESCRIPTION: Contentious issues are typically the language used to describe issues that deeply divide us. They tend to be those controversial topics that policymakers and administrators often try to shield from students, with the thought of maintaining safety and security. Yet, students live and interact with a world replete with contentious issues and struggle to make sense of and navigate their way through them. One question then becomes, to what degree do schools have a responsibility to engage in the discussion of controversial issues? What are the appropriate roles for contentious issues in the classroom to engage students in what’s happening in their community, state or nation? Participants will first learn how to utilize contentious issues as a springboard for facilitating effective dialogue and non-hostile discussions. Secondly, participants will learn how to harness the power of the contentious issue by developing authentic civic responsibility exercised in collaboration with youth and community partners.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.362 | 0.151 |
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; the direct Gemma label and the distilled Codex classifier 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".