Translating the climate crisis in the museum
Bibliographic record
Abstract
Translating Climate Change in the Museum Museum and Translation Studies are inherently multidisciplinary disciplines. Museums, like translations, rely on pre-existing ideas that they transform and share with new audiences in a new language. The idea of a “language of display” and of “museums as translations” has been developed by scholars such as Neather (2008), Sturge (2007), Valdeón (2015), and Liao (2016), who combine this metaphorical perspective with the analysis of interlingual translations. These are often present in museums to meet the needs of local multilingual audiences and/or tourists. This paper sets out to further explore this intersection of Museum and Translation Studies with the analysis of a case study. More specifically, this paper will find place in the broader context of sustainable humanities and question how the Museum of Natural Sciences in Brussels shares ideas related to climate change with its visitors. By focusing on a science museum, we hope to provide a new type of case study that might continue the discussion around museums and translations. This will be done through two approaches. First, texts in multiple languages will be compared to determine whether the translations reflect expected differences in reception across the different language publics. Secondly, the displays of the museum will be questioned for how they metaphorically translate the climate crisis. The results of these analyses will be crossed with information on the procedures of translation at the museum gathered through an interview, as well as with pre-existing visitors research. This will allow for a critical understanding of the goals and impact of translations in the museum.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".