Building Capacity for the Cultural Industries: Towards a Shared-Island Approach for Dance and Theatre
Bibliographic record
Abstract
Funded by the Irish Research Council’s (IRC) New Foundations Scheme 2021, under the Shared Island Initiative Strand (8) supported by the Shared Island Unit (SIU) in the Department of the Taoiseach, this report is the final output of a project that brought together new and existing academic and sector-based research to understand how all-island relations within the professional, publicly-subsidised performing arts of dance and theatre may be nurtured. The project was led by Dr Victoria Durrer (University College Dublin) in partnership with Dr Aoife McGrath (Queen’s University Belfast), and representatives from Theatre & Dance Northern Ireland, Arts Council Northern Ireland, Dance Ireland and Dylan Quinn Dance Theatre. Cavan County Council Arts Office, British Council Ireland and Arts Council Ireland / An Chomhairle Ealaíon are core research participants with Dr Emma McAlister supporting the work as Research Assistant. All of these individuals are referred to in this document as the Core Research Group. The professional, publicly-subsidised performing arts of dance and theatre are an interconnected ‘cultural industry’ on the island of Ireland. Very little documentation is available regarding the extent of these relationships, how this interconnection functions and with what impact. This lack of understanding limits how to consider strengthening, and also nurturing, these industries collectively and within the two jurisdictions of Northern Ireland (NI) and Ireland (IRL) in a post-Brexit and post- pandemic context. As a result, strategic development of the cooperative and competitive elements of Irish regional cultural industry development has been hampered. This absence is further concerning, especially as Brexit and the pandemic have posed strategic challenges to the livelihoods of a highly mobile cultural workforce and regional competition in cultural tourism. In this report, rather than focusing on SIU’s acknowledgement of the role of the arts, and dance and theatre, as ‘good work’ or a service for the endeavour of a ‘Shared Island’, we are concerned with the administrative systems, functions and operations of dance and theatre as an underpinning infrastructure of this endeavour. To date, public policies have largely neglected the labour of theatre and dance professionals, their aligned ways of working, their engagement with audiences, and the collaborative formal and informal working networks across Ireland.
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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 | 0.035 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.016 | 0.061 |
| Scholarly communication | 0.033 | 0.031 |
| Open science | 0.008 | 0.085 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".