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.iii This research, one of the first studies of Holocaust visual culture on Instagram, underscores the fluidity of Holocaust memory in the twenty-first century.While amateur photography at solemn sites has sparked concern, this dissertation demonstrates that though the number of Holocaust survivors become fewer in number, the act of remembering the genocide can be coded into the everyday behaviour of the amateur photographers featured in this work.This work not only shares authority with everyday publics in their efforts to remember and memorialize the Holocaust but reminds us that seemingly small and individual acts of remembrance can coalesce, contributing to a fluid and accessible archive of visual memory.A completed dissertation is the result of the hard work of a great number of people.First, I must thank the Department of History at Carleton University.Arriving to start my MA in Public History in the fall of 2011, I got a taste of something that I will not leave behind: a thoughtful, welcoming environment which treasures and encourages its students from their first day in the department.Most especially, to my supervisor, Dr. Jennifer Evans: thank you for eight
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".