Bibliographic record
Abstract
As the dramatic consequences of climate change finally begin to motivate governments around the world to explore how to move away from a dependence on fossil fuels, nuclear power is back on the agenda in the UK as a potential energy source. However, this new-found enthusiasm confronts a fundamental challenge—namely, that the radioactive wastes, accumulating since the very first nuclear power stations were built in the 1950s, have yet to be made safe for the long-term future. At the governmental level, there is a clear international commitment to the view that the most secure option for the management of radioactive waste matter is burial deep underground in an engineered geological disposal facility (GDF). Finland leads the international field, and the repository at Onkalo is expected to be fully operational by 2025. The Swedish government approved plans for the construction of an underground repository for spent nuclear fuel in 2022, with Canada, France, Japan, Switzerland, the UK, and the USA all actively engaged in siting and design initiatives. Strategies for generating public acceptance of geological disposal vary, as do the modes of engagement, the investments of time and money afforded, and the decision-making processes. These processes are conceptually and politically challenging. They require not only technical expertise and scientific understanding across an entire range of disciplines, but also the imaginative capacity to think across scales of time and space in what Ele Carpenter (2016: 14) has suggestively referred to as ‘reverse mining’.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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".