Bibliographic record
Abstract
This paper reviews the status of college-level hazards courses and updates the findings of a survey conducted a decade earlier. The surveys sought information regarding hazards courses taught by geographers throughout the United States and Canada. Instructors were asked whether they emphasize physical or social aspects of hazards, what specific physical hazards and human response topics are considered, and what hazards models or paradigms are discussed, among other topics. Information was gathered about the instructors' education and their involvement in hazards research. The majority of instructors altered their courses in response to Hurricane Katrina, yet an emphasis upon physical aspects of hazards continued in many courses. Only slight changes were noted in coverage of many physical and social aspects of hazards. Discussion of models of human response to hazards decreased over the decade. Textbooks are increasingly authored by geologists. Geographers who are actively engaged in hazards research, as shown by graduate theses and dissertations, publication of journal articles reporting hazards research, presentation of hazards papers at professional meetings, membership of AAG Hazards specialty groups and subscriptions to Natural Hazards Observer, significantly differ from other instructors in their approaches to teaching hazards geography coursework. They typically spend more class time discussing social aspects of hazards and human response models in their classrooms.
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 | 0.024 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".