Bibliographic record
Abstract
The museum is an inventive, globally and locally translated form, no longer anchored to its modern origins in Europe. Contemporary curatorial work, in these excessive times of decolonisation and globalisation, by engaging with discrepant temporalities—not resisting, or homogenising, their inescapable friction—has the potential to open up common-sense, ‘given’ histories. It does so under serious constraints—a push and pull of material forces and ideological legacies it cannot evade. This chapter explores the ‘times’ of the curator, both in terms of these times we live in, in which curatorial theory and practice seems to be ever-present, and a sense of the curator’s task as enmeshed in multiple, overlapping, sometimes conflicting times. It is concerned primarily with the later, the discrepant temporalities, or perhaps that should be ‘histories’, or even ‘futures’, that are integral to the task of the curator today. In contrast to the history of museum curating, curatorial work in recent years has been transformed by the re-emergence of indigenous cultures in former settler colonies which suggest the de-centering of the west. Drawing on research in the USA, Canada and the Pacific Islands, and analysing several diverse case studies and examples, the chapter explores examples of ‘indigenous curating’, that is to say, working with things and relations in transforming times. In doing so, it contributes to a world-wide debate, which this book is part of, about museums and the future of curatorship.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.050 |
| Scholarly communication | 0.028 | 0.025 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".