Reflecting on the Design and Implementation of a Corrosion Course
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
Knowledge of corrosion (degradation of materials involving electrochemical and other chemical processes) is important for many engineering and science disciplines. Up to 875 billion dollars could be saved globally if existing corrosion knowledge had been applied. Industry and education assessors have identified corrosion education as a key area of higher education currently lacking in many engineering programs. In this paper, we present the design of a course in corrosion and surface protection given to engineering students in different materials science and chemistry Master’s programs at KTH Royal Institute of Technology, Stockholm, Sweden. We discuss the course design in terms of the students’ learning approach, concept learning, perceived usefulness of the course, psychology of predicting one’s future responsibilities for potential corrosion failures, and the need for future educational developments. We recommend including actual and real corrosion cases in corrosion classes to increase corrosion awareness, concept learning, and long-term memory of corrosion problems and concepts. Teaching a sense of responsibility for future corrosion failures is a challenging task that demands alternative and innovative approaches.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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