FutureEverything: ArtsAPI:Research & Development Report
Notice bibliographique
Résumé
ArtsAPI was a research and development project on how to better understand and evidence the connectivity and connections that arts organisations generate between things, people and events. The outcome is a proof of concept business modelling and analytic tool to enable arts organisations to generate new insight from data. ArtsAPI was led by 3 partners: FutureEverything, is an art and innovation organisation that conceived and led the project; Swirrl, is a leading linked open data (LOD) developer with a long track record of partnership with FutureEverything; and University of Dundee, is a leading research university with a strong academic team who offered expertise in analysing networks of people, organisations and things. A wider group of arts partners were collaborators in the project. These were: Blast Theory Red Eye Culture 24 Forma Warwick Arts Contact Theatre Islington Mill Baltic What is it? ArtsAPI is a web application and an ambitious and experimental research and development project. The ArtsAPI web application is designed to enable arts organisations to show the value and impact generated through their networks. We believe that many arts organisations generate significant value through the relationships they create and sustain, but far too often this is not articulated or evidenced sufficiently to leverage insight, support and opportunities. ArtsAPI uses data to give organisations new insight into their relationships both internally and externally. This insight can be used  for business planning, marketing, programming and as a way of demonstrating your impact. The tool uses email data gathered from team members to generate the following: A visualisation of your network both on an individual and organisational level A list of all the organisations and individuals you are connected to both on an individual and organisational level A clustering tool which will allow you to analyse how well connected you are to different sectors (some manual input is required) A clustering tool which will allow you to analyse how well connected you are to different cities and countries (some manual input is required) It gives you insight into Keywords that you use in email exchanges. These keywords can indicate the types of activities of individuals in your network It gives you insight into the Social Network Analysis measure of ‘out degrees’ – a measure which demonstrates whom within the network is distributing information It gives you insight into the Social Network Analysis measure of ‘in degrees’ – a measure which demonstrates whom within a network is receiving information from other nodes It gives you insight into the Social Network Analysis measure of ‘Degree Centrality’ – a measure which demonstrates which nodes are central to a network in terms of control over information flow It gives you insight into the Social Network Analysis measure of ‘Density’ – a measure that gives some insight into the speed of information handling within the network Outcomes The main outcome of this ambitious and exploratory project is a proof of concept web application. This has been trialled and showcased through a roadshow, and the project themes have been communicated through an art commission. The ambitious and experimental research and development process has generated new findings, and also revealed significant challenges that further development will need to address.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,008 | 0,005 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,251 | 0,175 |
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 source (Gemma direct ou Codex distillé), 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 ».