Why palaeontologists must break the law: a polemic from an apologist.
Bibliographic record
Abstract
This essay, opinion piece - rant, even - is my personal protest about ill-conceived legislation enacted by national governments to 'protect' fossils (e.g. Germany, Brazil, Australia, Canada and China to name but a few ). Legislation that renders fossil collecting illegal is not actually about protecting fossils because it rarely achieves this aim. What it does do is curtail people's freedom to collect and own fossils� surely a thoroughly harmless pastime. Such legislation also stifles the assembly of aesthetically appealing, and potentially scientifically important collections that, over time, become part of the fabric of our scientific and cultural heritage (e.g. the fabulous Etches collection on the UK's Jurassic Coast World Heritage Site (Williams 2017)), and it surely stifles scientific endeavour: the fewer fossils that come out of the ground, the fewer there are for palaeontologists to work on, or the public to admire (in museums or in their own collections). Fossils are not part of any nation's heritage, palaeontological or cultural while they remain undiscovered in the ground. As a jobbing palaeontologist I want fossil collecting to be a pastime enjoyed by all, and it will only do so if everyone is permitted to collect, not just a privileged few.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.046 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.018 | 0.023 |
| Insufficient payload (model declined to judge) | 0.010 | 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".