More than "competent description of an intractably empty landscape": A Strategy for Critical Engagement with Historical Photographs
Bibliographic record
Abstract
In the early autumn of 1858, Humphrey Lloyd Hime set up his camera and darktent not far from what is now Winnipeg, Manitoba, coated a sheet of glass with collodion, and produced a view, which was subsequently titled, The Prairie, on the Banks of Red River, looking south (Figure 1). It presents a quintessential image of prairie topography, one which has come to be an enduring image of regional identity. In it, the landscape has been reduced to what Canadian novelist W.O. Mitchell has called “the least common denominator of nature”: earth and sky. In the context of geographical concerns for the way in which landscape images influence perceptions of place, and conversely, for the way in which perceptions of place influence landscape images, The Prairie ... looking south raises a number of questions: Why did Hime take this photograph and what was it intended to convey? Even more importantly, why is this photograph of interest to historical geographers, and how should we interrogate it? As a source of visual facts, what can it tell us about the landscape it depicts? As a form of visual representation, what can it tell us about the time(s) and place(s) in which it was created, circulated, and viewed? As an act of visual communication, what meanings (messages) were invested in it and generated by it? And, more generally, what can it teach us about critical engagement with the photograph in historical geography?
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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 | 0.050 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.031 | 0.071 |
| Scholarly communication | 0.025 | 0.031 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".