FutureEverything: ArtsAPI:Research & Development Report
Bibliographic record
Abstract
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.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.251 | 0.175 |
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".