Reuse of Wikimedia Commons Cultural Heritage Images on the Wider Web
Notice bibliographique
Résumé
Abstract
 Objective – Cultural heritage institutions with digital images on Wikimedia Commons want to know if and how those images are being reused. This study attempts to gauge the impact of digital cultural heritage images from Wikimedia Commons by using Reverse Image Lookup (RIL) to determine the quantity and content of different types of reuse, barriers to using RIL to assess reuse, and whether reused digital cultural heritage images from Wikimedia Commons include licensing information.
 Methods – 171 digital cultural heritage Wikimedia Commons images from 51 cultural heritage institutions were searched using the Google images “Search by image” tool to find instances of reuse. Content analysis of the digital cultural heritage images and the context in which they were reused was conducted to apply broad content categories. Reuse within Wikimedia Foundation projects was also recorded.
 Results – A total of 1,533 reuse instances found via Google images and Wikimedia Commons’ file usage reports were analyzed. Over half of reuse occurred within Wikimedia projects or wiki aggregator and mirror sites. Notable People, people, historic events, and buildings and locations were the most widely reused topics of digital cultural heritage both within Wikimedia projects and beyond, while social, media gallery, news, and education websites were the most likely places to find reuse outside of wiki projects. However, the content of reused images varied slightly depending on the website type on which they were found. Very few instances of reuse included licensing information, and those that did often were incorrect. Reuse of cultural heritage images from Wikimedia Commons was either done without added context or content, as in the case of media galleries, or was done in ways that did not distort or mischaracterize the images being reused.
 Conclusion – Cultural heritage institutions can use this research to focus digitization and digital content marketing efforts in order to optimize reuse by the types of websites and users that best meet their institution’s mission. Institutions that fear reuse without attribution have reason for concern as the practice of reusing both Creative Commons and public domain media without rights statements is widespread. More research needs to be conducted to determine if notability of institution or collection affects likelihood of reuse, as preliminary results show a weak correlation between number of images searched and number of images reused per institution. RIL technology is a reliable method of finding image reuse but is a labour-intensive process that may best be conducted for selected images and specific assessment campaigns. Finally, the reused content and context categories developed here may contribute to a standardized set of codes for assessing digital cultural heritage reuse.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,004 |
| 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 | 0,105 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».