Bibliographic record
Abstract
Drought is a complex subject that has varied definitions and perspectives. Although drought has historically been characterized as an environmental problem from both the meteorological and agricultural communities, it is not considered a sociological disaster despite its severe societal impacts. Utilizing the framework developed by Spector and Kitsuse (2011) and Stallings (1995), this research examines the process through which drought is defined as a social problem. An analysis of the data revealed drought was well covered in Africa, India, China, Australia, and New Zealand, yet very little coverage focused on the United States. There were less than 10 articles discussing drought and drought impacts in the United States. The workshops/meetings examined also were lacking in the attention to drought, although their overall theme was focused on hazards and resilience. Six sessions in over 16 years of meetings/workshops focused on the topic of drought, and one session was focused on the condition in Canada. The interviews uncovered five thematic areas demonstrating drought understanding and awareness: Use of outreach to get the message out; agricultures familiarity with drought; the role of drought in media; the variability of what drought is; and water conservation. Drought's claims-makers who are dedicated to providing outreach and education to impacted communities. Drought is often overlooked due to its slow onset and evolving development makes it difficult to determine when to engage in recovery efforts. Drought defined as a social problem also expands theoretical conversations regarding what events or issues should be included within the sociological disaster list of topics.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.018 | 0.035 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".