Four Conversations We Need To Have About Teaching and Learning in Canadian Political Science
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
Abstract Over the last 20 years of our careers, we have witnessed a significant decline in the respect for the liberal arts and social sciences. This decline forces us to regularly respond to questions about the relevance, value and future of our discipline. We know that political science provides fundamental skills for our students to succeed in the 21st century. Yet we also believe that as teachers and scholars we can do a better job of connecting to the realities of our students, articulating the skills and attitudes that our students will develop, and adapting our teaching methods to include high impact practices (Kuh, 2008) that focus on student learning. Here we argue that there are four important conversations that we need to have that can strengthen the appeal and interest in our discipline: the articulation of learning outcomes, the inclusion of high impact practices, attention to information literacy that recognizes the significant change in the delivery and access of research materials and a deep integration of indigenous ways of knowing in our teaching.
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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 | 0.046 | 0.188 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it