What's in a name? Measuring use and non-use of Digital Resources in the Arts and Humanities through Log Analysis Techniques
Notice bibliographique
Résumé
September 3-6, Dartington College of Arts. The LAIRAH (Log Analysis of Internet Resources in the Arts and Humanities) project aims to determine whether, how and why digital resources in the humanities are used, and what factors might make them usable and sustainable. Based at UCL and funded by the UK Arts and Humanities Research Council (AHRC) ICT Strategy scheme, LAIRAH is a year long study which will analyse patterns of usage of online resources through server log analysis (http://www.ucl.ac.uk/slais/LAIRAH/). Log files have been provided by the three online archives supported by AHRC: the AHDS Arts and Humanities Collection (http://www.ahds.ac.uk/); Humbul Humanities Hub (http://www.humbul.ac.uk/) and the Artifact database for the creative and performing arts (http://www.artifact.ac.uk/). The search logs show patterns of which resources are interesting to users, and in the case of the AHDS (which provides links to resources themselves), which ones users visit. Method Despite the creation of over three hundred digital resources funded by the AHRC alone, there is little robust evidence regarding which are used and which neglected.[1] We are therefore analysing a year's worth of transaction log data (a record of web page use automatically collected by servers) from each resource. This provides the words searched on (search logs), the pages viewed (user logs), the web site that the user has come from (referrer logs), and basic, but anonymous, user identification tags, time, and date stamps. We have also designed short online questionnaires, covering user characteristics and perceived outcomes, which will be compared with actual search and usage patterns: research undertaken by UCL's CIBER team has shown a discrepancy between resources actually used, and those that users report having visited. [2] Findings Whether or not they come from an academic domain, most users search for certain resource subjects: war, witchcraft, fashion, suffragettes, war poetry, Shakespeare and census data. More obscure subjects had much lower levels of use, and a worrying number appear never to have been accessed. The title of the resource proved surprisingly important: resources with the words 'census data' in the title are used, whereas one called 'enumerator returns' remained neglected. We found that users from academic domains tend to be more persistent, searching for longer and accessing more resources than those coming from commercial servers. The most heavily used areas of the AHDS central website are those giving information about depositing resources rather than the use of already archived resources. This suggests that digital humanities may still be in a phase of creation rather than re-use of resources. Conclusion Use levels alone cannot be an absolute measure of a resource's quality or usefulness. Some digital humanities resources are created as pure research, or for testing technical solutions, and should not necessarily be measured by their use levels. Equally a resource that is very valuable for a small community may not be widely used. However if a large potential audience ignores a seemingly useful resource, we need to understand why. It is these issues that we will address in the remainder of the project and will report upon in this paper. References [1] Warwick, C., Blandford A. and Buchanan, G. (2005) User Centred Interactive Search: A study of humanities users in a digital library environment. Presented at the Association for Computers and the Humanities- Association for Literary and Linguistic Computing, Conference 2005. University of Victoria, Canada, June 15-18. [2] Huntington P, Nicholas D, Williams, P. (2003) 'Characterising and profiling health web users and site types: going beyond hits'. Aslib Proceedings 55 (5/6): 277-289
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 | 0,004 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,006 |
| Science ouverte | 0,001 | 0,001 |
| 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 ».