Bibliographic record
Abstract
This article presents an innovative method for researching quotidian photography, particularly Real Photo Postcards (RPPC), by combining performance studies and archival sciences with oral narrative. The Nuanced Archival Triangulation (NAT) method culminates evidence from public records, newspapers, and local and non-traditional archival repositories with living family stories, resulting in a more nuanced approach to understanding people featured in visual archives. The NAT method extends and adapts Hulme’s methodology for researching textual archives of queer defendants in the United Kingdom in the early 20th century while also including Pennavaria’s genealogical methods that involve the family in historical research. The NAT methodology is comprised of four steps. Step one begins with examining traditional genealogical records of the person studied from the RPPC via Ancestry.com. Next, newspaper archives are accessed to uncover information about the individual’s social life to contextualize the biographical information found in step one. Then, guerilla research is used to locate non-traditional, undigitized evidence related to the studied person. And finally, that person’s family is contacted to solicit personal artifacts and family stories that illuminate the person’s lived story to share agency with the living relatives of the primary RPPC subject. This paper employs the NAT method in a case study centering on an RPPC of Dale Smith and Alvin Ruddick, two Navy sailors who served in WWII. By locating relevant biographical evidence, speculation about the subjects' sexual identities is investigated in the RPPC. This paper concludes by discussing how the NAT methodology can amplify marginalized communities' visual archives.
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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 | 0.066 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".