Transparency, Testing and Standards for Archaeological Predictive Modelling
Notice bibliographique
Résumé
: This paper starts with considering the extent of archaeological predictive modelling in Europe and the various different techniques used. One of the main criticisms against archaeological predictive modelling is that it is often viewed as a ‘black box’ technique and this paper suggests one possible way to make the procedure more transparent to the non-technical user and allow that user to test and interrogate a model. The paper stresses that, if possible, archaeological predictive models should be tested against new archaeological data, as opposed to how well a model predicts known archaeological data. The paper also suggests that the best way of justifying the use of the archaeological predictive modelling for cultural heritage management is by directly comparing the costs and results from the technique against the existing system of cultural heritage management used. This paper argues that whilst it would be impractical to write standards that cover every technique to produce a predictive model, it would be advantageous to start thinking now about standards for the output of these models. Thus, one model could be directly compared to another model and standardised attribute data could be exchanged between models and other applications. Keywords: Archaeological Predictive Modelling, Transparency, Testing, Standards The Extent of Archaeological Predictive Modelling In Europe’ In late 2011, the words ‘archaeological predictive modelling’ followed by the name of each of the countries of Europe were entered into the Google search engine. Thirty six countries (72%) had reference to research into archaeological predictive modelling within that country, fourteen countries (28%) had no reference to archaeological predictive modelling (Fig. 1) and twelve countries (24%) had reference to the technique being used (in part) for cultural heritage management. However, just because there was no reference to research into, or the use of, archaeological predictive modelling on the internet, does not mean that it does not exist in that country. Hence, the above figures are probably conservative. Internet references for research into archaeological predictive modelling were also found in Australia, the USA, Canada, parts of Africa, etc. The conclusion from this provisional survey is that there is a lot of interest in the technique world-wide and that some countries are starting to incorporate the technique into their systems of cultural heritage management.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».