Bibliographic record
Abstract
One of the criteria for the evaluation of undergraduate instructors by their students is often "Did the instructor stimulate my interest in the subject matter of the course?" Instillation of passion in science for undergraduates can start with passionate instructors. It was with this reasoning, and the impetus of the International Year of Crystallography, that I undertook the holistic redesign of a required third year Honours course on Chemical Literature and Scientific Communication at Wilfrid Laurier University, in Waterloo, ON, Canada. The course outcomes were based on recommendations for information literacy [1], and included the analysis of complex ethical problems, understanding the organization of the library, and the demonstration of critical thinking by evaluation of information from literature, and with help from university librarians and learning services, the broad exploration of crystallography was integrated throughout. The students were then tasked to share their new skills in communication, and knowledge of crystallography, in a public poster conference, attended by 400 first year science undergraduates. This talk will highlight the impact of peer-share crystallographic education, as well as other integral activities in first year introductory and senior inorganic chemistry courses.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".