‘A Portrait of Lower Silesia’: Researching identity through collodion photography and memory narratives
Bibliographic record
Abstract
In this article, I discuss a performance arts–based visual methodology based on the use of the archaic wet collodion photography. The collaboration between Street Collodion Art photography collective and myself, as a researcher, had two aims: to generate a large scale photographic and narrative portrait of Lower Silesia in Poland, and to explore identities in the region where nearly all of its inhabitants represent recent migrant populations. Data generated through this project include collodion portraits, their interpretations and narratives collected through unstructured interviews. Initial data analysis has generated identity narratives linked to work, place and belonging and ethnicity/nationality. In addition, in 2016 and 2017, three exhibitions of the portraits and a selection of edited stories took place in Lubin, Legnica and Wrocław attended by local inhabitants, including project participants. The examination of the arts-based methodology finds that the ritual character of the wet collodion photographic encounter has acted as a form of artistic intervention which, in generating memory narratives, enabled an articulation of social identities in the climate dominated by nationalist discourses. Such symbolic work emerging out of the project reveals a critical potential in the collaboration between the arts and social research. Furthermore, the project has shown that despite different traditions of practice, a collaboration between the artists and social researchers can yield rich data and access participants in ways that conventional methodologies cannot.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".