Quantitative analysis of open-source data on metal detecting for cultural property: Estimation of the scale and intensity of metal detecting and the quantity of metal-detected cultural goods
Bibliographic record
Abstract
Through netnographic analysis of online forums and social networks, this study presents quantitative analysis of the scale and intensity of metal detecting and the quantity of metal-detected cultural goods. It adapts open-source data to develop empirical measures; to ensure reliability and consistency of sourcing and interpretation, these data were drawn from English-language forums and networks. Based on a poll of 668 online community members, it infers the size of active detecting communities from the size (93.42%) of online detecting communities. Based on open-source data on the detecting practices of 101 detectorists, the worst tolerable weather for 151 detectorists and seasonal variations in the reporting of 1,089,337 finds to the Portable Antiquities Scheme over 13 years, it determines a pragmatic minimum average of 286.02 h of detecting per person per year. Comparing activity in a wide range of permissive, restrictive and prohibitive regulatory environments - based on local-language forums and networks in Australia, Austria, Flanders and elsewhere in Belgium, Canada, Denmark, England and Wales, Ireland, the Netherlands, New Zealand, Northern Ireland, Scotland, and the United States - it finds that permissive regulation is ineffective in minimising harm to heritage assets, whether in the form of licit misbehaviour or criminal damage. Restrictive and prohibitive regulation appear to be more effective, insofar as there is less overall loss of archaeological evidence.
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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 | 0.016 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".