Conservation Science Education Online (CSEO) – A heritage science resource
Bibliographic record
Abstract
Abstract Conservation Science Education Online (CSEO) is a new online resource that shares strategies for teaching science in art conservation and related cultural heritage fields. An overview will be given of how undergraduate chemistry curricula in the United States have used examples from cultural heritage. The field of art conservation will then be described with an emphasis on the science curricula taught in art conservation programs around the world. Challenges include relating theoretical learning to real-world applications and teaching scientific terminology and concepts to students who may have limited science backgrounds; as well, there is a lack of textbooks and resources with appropriate case studies. The newly launched CSEO online resource offers freely available, effective teaching methods in the form of modules developed by international educators in the field. The inaugural CSEO Conference 2022 served as an introduction to the online resource for a global audience and was the first dedicated conference to bring together heritage science educators to discuss challenges and teaching strategies with the goal of building such a resource. The conference facilitated discussions among participants about teaching strategies, with the intention that these topics would become modules for the online resource, available to all science educators.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.256 | 0.061 |
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".