Disrupting archives: Empire, extractivism, and the visual trace in photographs of rural agricultural Puerto Rico, 1941–1942
Bibliographic record
Abstract
Abstract Archives can be rich sources of information, yet they are also very often built within the violent processes of empire‐building, setting the stage for how knowledge about colonised places are constructed, disrupted, and how their histories are understood. Archives often tell us more about power and the kinds of knowledge that were important to imperial powers than the people and places disrupted by empire. This necessitates careful consideration of the archive itself, and not simply the information it contains. The relationship between the visual aspects of the archive and the ways that we come to know about colonised sites is the focus of this paper. Focusing on photos of rural Puerto Rico taken after The Depression (circa 1941–1942), this paper builds an understanding of archives as sites that can be transformed into conceptual or imaginary space that exists outside of the original purposes of the archives. This space is reliant on the willingness of those who encounter archives to read beyond what they are presented with. This in turn allows for careful reading of the traces and possibilities inside archives that subvert their seemingly totalising narrative.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".