Bibliographic record
Abstract
The Materiality of the Archive is the first volume to bring together a range of methodological approaches to the materiality of archives, as a framework for their engagement, analysis and interpretation. Focusing on the archives of creative practices, the book reaches between and across existing bodies of knowledge in this field, including material culture, art history and literary studies, unified by an interest in archives as material deposits and aggregations, in both analogue and digital forms, as well as the material encounter. Connecting a breadth of disciplinary interests in the archive with expanding discourses in materiality, contributors address the potential of a material engagement to animate archival content. Analysing the systems, processes and actions that constitute the shapes, forms and structures in which individual archival objects accumulate, and the underpinnings which may hold them in place as an archival body, the book considers ways in which the inexorable move to the digital affects traditional theories of the physical archival object. It also considers how stewardship practices such as description and metadata creation can accommodate these changes. The Materiality of the Archive unifies theory and practice and brings together professional and academic perspectives. The book is essential reading for academics, researchers and postgraduate students working in the fields of archive studies, museology, art history and material culture.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.020 | 0.085 |
| Scholarly communication | 0.038 | 0.027 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.005 | 0.006 |
| 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".