The Sustainability of Geological Mapmaking: The Case of the Geological Survey of Great Britain
Bibliographic record
Abstract
Henry De la Beche's leadership of the Geological Survey of Great Britain in the second quarter of the nineteenth century led to the establishment of a number of key institutions which ensured the Survey of survival beyond the initial phase of geological mapmaking. Considered as a finite activity serving only to fix on paper the spatial distribution of an unchanging physical resource, geological mapmaking alone was never a secure basis for institutional or disciplinary development. The actions taken by De la Beche in the 1830s and 1840s, at a time when public and politicians alike were suspicious of government-funded science, were echoed 150 years later by successors who served governments with similar doubts about non-commercial scientific activity. Whether buried within an empire of public institutions, illuminated in museum collections which spoke of utilitarian value, or conceptualised as an income-generating database of rare data, the continuation of geological mapmaking in Britain relied upon a relationship to, and relevance for, a wider world of politics and practice. Seen in the long view, the British Geological Survey demonstrates that a nation can only make and re-make geological maps if that activity can be submerged within, or repackaged as, a new strategically-valued socio-economic initiative.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".