Networks of memory: Vernacular photography, (new) media, and meaning making
Bibliographic record
Abstract
Vernacular photography can be broadly defined as “ordinary photographs, the ones made or bought (or sometimes bought and then made-over) by everyday folk from 1839 until now” (Batchen, 2001, p.57). At first glance, with digital media and online communication technologies that allow us to send and receive countless images on a daily basis, contemporary social conventions associated with vernacular photography appear vastly different than they did in the mid-nineteenth century. What persists in the use (and reuse) of vernacular photographs is how they are called upon in meaning-making activities to help understand the past in and for the present. In this dissertation I examine meaning-making activities linked to recalling and reflecting on the past in specific ways: how historical exhibitions of vernacular photographs have influenced current practices of online exhibition; and how vernacular photographs are remediated and taken up in memory practices involving two particular projects, Collected Visions and Dear Photograph, that display crowd-sourced vernacular photographs in both gallery and online spaces. My research is informed by Actor-network theory (ANT) approaches that emphasize how action takes place in nodes where different actors meet and influence one another (Latour, 2005). Vernacular photographs and their exhibitions are the result of complex interactions between people, media, and technologies where information and meaning making is transformed, translated, and modified (Latour, 2005, p. 39). Research for this dissertation included visits to museums and archives and interviews with artists and curators who work with vernacular photographs. The variety of methods employed complement one another and allow for a type of ‘process-tracing’ where a variety of different data from different sources are examined to consider “the links between possible causes and observed outcomes” (George & Bennett, 2004, p. 6). Through analytical ‘origin stories,’ I present narratives of Collected Visions and Dear Photograph tracing how vernacular photographs are used, remediated, and displayed in ways that allow for the possibility of online spaces of exchange. I then offer ‘microstories’ that describe encounters with specific images and texts in Collected Visions and Dear Photograph in an effort to document memory work processes that emerged during my research.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".