Re-vitalizing the First Year Class through Student Engagement and Discovery Learning
Bibliographic record
Abstract
The first year course in Sociology at Mount Allison University introduced students to social issues via dynamic class interactions and assignments that are designed to build conceptual and applied skills. Developments to the course organization have maximized the opportunities for discovery learning and have made the class an enjoyable teaching experience. This article will outline the core innovations that have been developed: 1.) A workbook, similar in style to a hands-on science lab manual, has been developed to engage students in active in-class discovery learning projects. 2.) Client-based interactive class activities are used to help students engage in the solving of contemporary social problems in a manner that reveals the contemporary relevance and application of knowledge regarding social problems. 3.) Research assignments are provided through an internship at the simulated ESPRIT (Evaluating Social Policy Research Investigation Team) think tank which provides students with the opportunity to develop research and analysis skills that are relevant to careers in the field of social analysis. 4.) The course includes the analysis of a contemporary best-selling book that addresses a relevant social problem so that students have the opportunity to participate in current debates about issues of social importance.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".