Enhancing applied, interdisciplinary learning with online, interactive case history modules that bridge undergraduate geoscience courses
Bibliographic record
Abstract
Proposal submitted to the University of British Columbia (UBC) Teaching and Learning Enhancement Fund (TLEF) 2015 call. Project Summary Complex geoscience problems often cross disciplinary lines. As such, when students become professionals, they will be required to draw upon and connect concepts from a range of disciplines. However, students are given little institutional or instructional support to build this web of connected concepts; most courses are taught in isolation. We propose to support students’ exploration of these connections using multi-disciplinary touchstones that connect six courses across the disciplines of geology, geophysics, geological engineering and hydrogeology. We will develop these touchstones using interdisciplinary case histories: scientific narratives that begin with an applied problem, incorporate data and analyses, and conclude with inferences and solutions to the stated problem. This project will create online modules, with embedded interactive simulations, which enable students to examine the scientific concepts and connections between these concepts within the context of each case history. Interdisciplinary teams of students will be employed in the development of these modules.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.010 |
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".