Teaching Information Literacy in the Emerging and Dynamic Fields of Preparedness and Security
Bibliographic record
Abstract
Emergency preparedness and security are comparatively new academic fields, but they have grown tremendously in the last ten years.1 Colleges and universities all over the United States and Canada are adding and expanding programs dedicated to various facets of these subjects. While the specific subtopics and focal areas can vary by program, they all explore threat assessment, risk management, and disaster mitigation in some capacity. Emergency preparedness or emergency management programs prepare students to pursue careers dealing with crisis mitigation and response, whether that crisis is a result of natural disaster, climate change, war, civil unrest, infrastructure failure, economic instability, cyberattack, or some other cause. Security programs similarly address risk and mitigation, although those programs and classes tend to focus more directly on human-caused threats such as terrorism, radicalization, and violence. Often, preparedness and/or security programs align with companion studies in environmental management, international relations, informatics and data analysis, public health, and so on. However, because preparedness and security have only recently been considered as independent fields of study, they lack the reservoir of resources available to better established academic areas.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".