"The Curiosity Project”: Re-igniting the Desire to Inquire and Transformation Through Intrinsically-Motivated Learning and Mentorship
Bibliographic record
Abstract
The Curiosity Project encourages students to engage in intrinsically-motivated, inquiry-based project learning, investigating topics that interest them and following them down long and often winding roads, where u-turns, hidden side roads, and venturing off the map are a cause for excitement not a distraction from the destination. These inquiry-based, semester-long, student-led projects incorporate proximal goals of weekly learning logs and peer group meetings and in-depth feedback from peers and senior student facilitators. There are no page/word limits or minimum/maximum number of resources, just students' burgeoning sense of what constitutes high-quality work. Community-oriented projects round out this experience. In this paper, I outline the structure of The Curiosity Project, its theoretical underpinnings, and present both quantitative and qualitative data that suggest that not only are the project's basic goals being met but that there are unexpected, but theoretically supported, transformational properties stemming from student engagement in this project.
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".