Curating Viking objects through customized scrolling: How search engines personalize historical narratives
Bibliographic record
Abstract
The stories that museum objects convey change on social media. This narrative shift is analyzed through the development of social media methods that track the dissemination of the Viking helmet from the Swedish History Museum to personalized YouTube search engine result pages (SERPs). Social media methods are used to compare the narratives and meanings produced on YouTube SERPs with the narratives and meanings sanctioned by the Swedish History Museum. The hermeneutic process at the core of the methodological approach reveals how personalization algorithms targeting user identities through IP addresses influence the meanings of objects, which are mediated and commercialized via social media SERPs. This research demonstrates that personalized search engine result pages have an algorithmic impact on the narratives of objects that counters the social justice goals integral to contemporary museum practices. The meaning curation of museum objects is not customized to individual preferences and identities. Museums advance selected interpretations of objects to endorse a variety of vantage points so that the fixity of identity categories can be questioned.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".