Introducing cultural loss to the sustainability agenda
Bibliographic record
Abstract
Anthropogenic climate change and ever-increasing land-use are driving an environmental transition the speed and likely end-state of which are unprecedented in the recent past. The amplitude and magnitude of these changes constitute a clear and present challenge to human societies at scales that range from local to global. Central in the ability of any society to weather these challenges and to build sustainable futures are their inherited knowledge, behaviours, and technologies - in short, culture. However, climate-induced shocks may lead to cultural loss, which in turn may further aggravate both impacts and the ability to recover and rebuild. While reports of cultural loss abound, the formal conditions of such loss have never been investigated. Relevant insights and data are distributed across multiple disciplines, and are poorly connected. Despite the urgency to better understand the drivers of cultural loss, there is currently no systematic exploration of the underlying mechanisms that govern this process. We argue here for the need to develop a structured, interdisciplinary, and model-driven focus on exploring the causal links between sustainability, resilience, and the susceptibility of culture to shock-driven loss. In doing so, we highlight the urgent need to build bridges between different academic disciplines, policymaking, and communities susceptible to cultural loss.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".