Bibliographic record
Abstract
Problems of earth science involve not only questions of physical science and engineering, but also of the social sciences and humanities. As a fourth-year geo-environmental engineering student, I am increasingly concerned that issues of philosophy and public policy are being neglected in earth science classrooms. Realizing that my scholastic career was narrowing, I sought an opportunity to alleviate this concern and expand my horizon. This summer I participated in just such an opportunity: the Global Climate Change and Society Program, a Research Experience for Undergraduates (REU) sponsored by the National Science Foundation and held in Boulder, Colorado. Throughout the program, students were encouraged to collaborate with peers from around the country, exposed to an outstanding roster of guest speakers, and inspired to reap practical knowledge in disciplines outside their area of specialization. Canadian undergraduates in Earth science would greatly benefit from participating in educational programs of a similar nature. Canada's closest analogue of the National Science Foundation, the Natural Sciences and Engineering Research Council, needs to expand its outreach programs and spearhead a campaign to develop Research Experiences for Undergraduates in Canada.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.103 | 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".