Winter Storm Uri: Resource Loss and Psychosocial Outcomes of Critical Infrastructure Failure in Texas
Bibliographic record
Abstract
In February of 2021, Winter Storm Uri affected parts of the United States, Mexico, and Canada. Texas was particularly hard hit, as the state's primary power provider, ERCOT (the Electric Reliability Council of Texas), proved to be unprepared for the event—despite similar storms in 1989 and 2011 that revealed weaknesses in the state's electric grid system. This article investigates psychosocial outcomes of individuals who experienced Winter Storm Uri. Drawing upon survey data collected in Texas in April and May of 2022, we illustrate ways in which loss of critical infrastructure and compounding results influence levels of stress among respondents. Using Hofoll's (1989, 1991) Conservation of Resources model of stress, we find that Uri‐related losses of objects and conditions resources contribute to elevated stress as measured by the Avoidance subscale of the Impact of Event Scale (Horowitz 1976; Horowitz, Wilner, and Alvarez 1979)— more than one year after the disaster. Our regression model consisting of indicators of objects resource loss, conditions resource loss, and demographic characteristics explains approximately 33 percent of the variance in the Avoidance subscale. Findings suggest that more attention should be paid to the social impacts of critical infrastructure failures and that such impacts should be addressed by improving critical infrastructure policy and regulations, as well as the physical structures.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".