Dimensions of Recreancy in the Context of Winter Storm Uri
Bibliographic record
Abstract
Winter Storm Uri damaged parts of the United States, Mexico, and Canada in February of 2021. The State of Texas was heavily affected due to the institutional failure of Texas's primary power provider, the Electric Reliability Council of Texas (ERCOT). Despite similar previous storms that exposed weaknesses in the state's power grid system in 1999 and 2011, ERCOT did not make the necessary changes to prevent a future disaster. The purpose of this study is to advance the understanding of the concept of recreancy through the exploration of eight different dimensions of the concept: trust or distrust in institutions; institutional responsibility for disaster preparedness; responsibility for impacts of a disaster; effectiveness or ineffectiveness of institutions in responding to a disaster; an institution's capability of preventing a similar event in the future; an institution's willingness to make changes in their actions or behavior; confidence that an institution will prevent a similar event in the future; and responsibility for compensation for impacts of a disaster. To examine the composition of the concept of recreancy, I analyzed survey data collected in Texas during April and May of 2022. I aggregated and coded survey data according to the level respondents reported to agree with the survey indicators measuring dimensions of recreancy. I utilized Confirmatory Factor Analysis to analyze if the derived dimensions of recreancy measure recreancy, and if some are more salient than others. Confirmatory Factor Analysis revealed variability in the importance of different dimensions of recreancy, suggesting that some dimensions are more salient than others in shaping residents' perceptions of recreancy in the context of Winter Storm Uri. Further analysis revealed a preliminary model to operationalize recreancy, however further analysis is needed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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 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".