Bibliographic record
Abstract
In the fall of 2023, I embarked on a transformative journey that transcended the traditional boundaries of instructorship.As one of the instructors of a massive STEM course, I found myself not merely imparting knowledge but actively collaborating with two student partners, Kelsey and Samuel, to enhance the learning experience for both current and future students.Involving Kelsey and Samuel in a structured collaboration was key for me to ensure student voices are heard in our course decisions.While end-of-semester evaluations provide valuable feedback, they can be biased and limited to a particular point in time.This student-instructor partnership approach complements course surveys by providing more profound and immediate insights.Our focus centered on the course's recitation component-the bridge between theoretical concepts discussed in lectures and real-world problem-solving.Our journey unfolded on two distinct fronts.First, my collaboration with Kelsey led us to explore the dynamics of inperson group work, a new landscape for this course.We observed and analyzed how students engaged with one another and exchanged ideas, as well as the supportive environment that facilitated these interactions.The insights we gained informed our strategies for fostering effective collaboration.Second, Samuel and I addressed the intricate landscape of artificial intelligence (AI) policies.As AI becomes increasingly integrated into education, responsible usage is paramount.Together, we explored methods for introducing students to AI responsibly while highlighting its inherent limitations.Our joint efforts revealed the profound potential inherent in faculty-student partnerships.We brought diverse perspectives and expertise to the table, transcending hierarchical roles.Our initial insecurities gave way to empowerment, transforming concerns into shared purposes.Reflecting on this journey, I recognize that it doesn't belong solely to me as an instructor.It belongs to all who dare to learn together-the chorus of voices shaping transformative education.Thus, this reflective essay considers the insights of Kelsey, Samuel, and myself (Beatriz).Looking to the future, I wholeheartedly embrace the opportunities for collaboration between students and instructors, recognizing that diverse perspectives from both parties are essential for enhancing learning experiences, even in the most crowded classrooms.What follows is a reflection on our partnership from the perspectives of each of us involved.First, we set the stage by describing the setting of the partnership, and second, we share each of our
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Other design | low |
| grok | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| opus | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".