Homeland security and emergency management in institutions of higher education (IHE): Texas case study
Bibliographic record
Abstract
This exploratory case study adopted the classical content analysis (CCA), cross case mixed strategy and Qualitative Data Miner (QDA) correspondence analysis of websites across 41 accredited public Institutions of Higher Education (IHE) in the state of Texas.The conceptual framework guiding the study was based on adaptive resiliency and Disaster Resilient University (DRU) paradigms.The goal of this inquiry was to determine (a) the most common organizational arrangements adopted across the IHEs in Texas to integrate emergency management functions on their campuses?;(b) the scope of most common activities and engagement performed by IHEs in Texas with respect to supporting the DRU mission across the cycle of emergency management?; (c) competencies and job definitions of officials specifically involved in supporting the all-hazards approach to emergency management on IHE campuses in Texas?;(d) training, exercise and certification standards as well as emergency notification systems commonly adopted among the IHEs in Texas; and (e) the type of educational, awareness, and outreach programs which can be identified across Texas IHEs in support of the DRU mission?Common and divergent themes, as well as implications for IHE leadership practice will be discussed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".