Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Everyday people make use of Instagram to visually share their experiences encountering Holocaust memory. Whether individuals are sharing their photos from Auschwitz, the United States Holocaust Memorial Museum, or of the Memorial to the Murdered Jews of Europe in Berlin, this dissertation uncovers the impetus to capture and share these images by the thousands. Using visuality as a framework for analyzing how the Holocaust has been seen, photographed, and communicated historically, this dissertation argues that these individual digital images function as objects of postmemory, contributing to and cultivating an accessible visual and digital archive. Sharing these images on Instagram results in a visual, grassroots archival space where networked Holocaust visuality and memory can flourish. The Holocaust looms large in public memory. Drawing from Holocaust studies, public history, photography theory, and new media studies, this dissertation argues that the amateur Instagram image is far from static. Existing spaces of Holocaust memory create preconditions for everyday publics to share their encounters with the Holocaust on their own terms. Thus, the final networked Instagram image is the product of a series of author interventions, carefully wrought from competing narratives and Holocaust representations. The choice to photograph, edit, post, and hashtag one's photo forges a public method for collaborating with hegemonic memory institutions. This work brings together seemingly disparate sources to find commonality between Instagram images, museum guestbook entries, online reviews, former concentration camps, and major Holocaust memorials and museums.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it