Bibliographic record
Abstract
Future Memory Work addresses a crucial challenge in contemporary pluralistic societies: the organisation of open, participatory and socially inclusive memory practices in digital media ecologies. It brings a novel relational approach to future memory work across institutions, people, and modalities. Advancing inter- and transdisciplinary research and rich empirical cases from across Europe and beyond, the book examines how memory practices in digital media are open for engagement of people with diverse backgrounds. It analyses the modalities of memory making and how they can enable institutional and public memory making with a broad spectrum of people and groups in civil society at local, translocal, national and global levels. The chapters examine the mediatized character of memory making, whilst also critically considering what obstacles and potentials emerge from participatory memory work. As a whole, the book is a comprehensive source of knowledge and ideas for creating socially inclusive, sustainable memory practices and futures. It sets the multidisciplinary research agenda for advancing studies of heritage in contemporary digital media as an element and a driver of cultural and social change. Future Memory Work is essential reading for academics, students and professionals working in the fields of Anthropology, Museum Studies, Digital Cultural Heritage, Memory Studies, Cultural Studies and Design.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 0.014 |
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".