Beyond Window Rainbows: Collecting Children’s Culture in the COVID Crisis
Bibliographic record
Abstract
As COVID-19 dramatically alters the museum sector, museums and archives are implementing collection initiatives that will have tremendous influence over how the pandemic is understood and remembered. As collections experts, museums are leading the charge to document, collect, and interpret our current circumstances as they unfold in real time, relying more than ever on public participation and crowd-sourcing. A key development in such rapid-response collecting has been the interest in and solicitation of contributions that document the current crisis. Yet, initiatives that target young people remain few and far between, and often reproduce romanticized and reified understandings of children and childhood that reflect a longer history of excluding children’s voices from museum collections and society at large. This paper will explore museums’ collection of children’s culture in various forms with attention to the epistemological and ethical challenges that such initiatives entail. We argue that children are crucial citizens whose knowledge, perspectives, and experiences must be collected and preserved during this historic moment and beyond, in ways that attend to the particular circumstances they face as multiply marginalized museum constituents and members of society.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.024 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".