Endangerment-driven heritage volunteering: democratisation or ‘Changeless Change’
Bibliographic record
Abstract
This article is the product of prolonged wrestling with the question of how heritage professionals and researchers can facilitate and sustain public agency in caring for heritage in the UK during austerity without exploiting volunteers or devaluing professionals. It offers critical perspectives on efforts made to democratise heritage in the UK by increasing public participation through a critique of neoliberalism and the rise of neoliberal approaches in the heritage sector. It argues that the adoption of neoliberal approaches, such as crowdsourcing, that profess to democratise yet reinforce existing power structures, is the inevitable result of insisting on protecting material culture from harm, despite the continuing accumulation of more ‘heritage’. Drawing on critical perspectives on participation from a number of disciplines, it is suggested that efforts to increase public participation in heritage cannot hope to avoid exploiting volunteers, devaluing professionals and marginalising traditionally underrepresented demographics unless they also let go of the perceived need to protect the materiality of the past. Drawing on Sarah May’s archaeology of contemporary tigers, this article argues that the application of endangerment narratives to heritage reinforces uncritical understandings of both heritage and volunteering that preclude heritage from fulfilling its potential function as a contemporary social process.
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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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.095 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".