Flood Photography and the Visual Component of Environmental American Studies
Bibliographic record
Abstract
This article develops a new approach for using photographic sources that might be of interest to American Studies scholars whose research contributes broadly to environmental education. Over the past forty years of photographic scholarship, scientific and other record images have become relatively prominent as primary sources. This visual material can be used to interrogate past responses to flooding and other environmental events. On the other hand, discourses around social documentary continue to frame how the human impacts of rapidly changing environments are visualised. By comparing two sets of images from the 1930s, the article juxtaposes the approaches of photographers associated with these two conventionally distinct areas to offer a more rounded view of flood photography. The discussion starts with a reflective section detailing how I arrived at my current research project. Following this, the categories of scientific and social documentary photography are described relationally in the context of the agencies of the New Deal, in the process setting out an argument for the contribution that engaged visuality can make to Environmental American Studies. Afterwards, the attention shifts to focus on images from two official contexts. The first example concerns record photography from the Soil Conservation Experiment Station in Bethany, Missouri, whilst the second considers photographs that the Resettlement Administration produced in response to flooding in Posey County, Indiana, in 1937. The article concludes by remarking on some of the implications of this method for how American Studies researchers currently conduct environmentally focused projects.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".