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Record W7065211892

Deep into the Province:ASSESSING THE VALUES OF CULTURAL NETWORKS IN THE NORTH OF THE NETHERLANDS

2023· article· en· W7065211892 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Groningen research database (University of Groningen / Centre for Information Technology) · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicX-ray Spectroscopy and Fluorescence Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMetropolitan areaThe artsQuarter (Canadian coin)PopulationCultural ecologyRural areaSustainable developmentCreative Cities
DOInot available

Abstract

fetched live from OpenAlex

It is widely acknowledged that the contribution of arts and culture to sustainable development of peripheral regions differs from culture-led development strategies in metropolitan areas (see e.g. Duxbury 2021, Lysgård 2016, Van den Hoogen 2019). Empirical study has e.g. found that on an individual level the values of rural artists differ from those living in urban areas (Stevenson, 2018), most prominent being that they have less interest in wealth accumulation, and that they avail of a network of professional relations that connects these rural artists to an ‘economy’ (Duxbury 2021) or ’ecology’ (Holden 2015, Bartleet et al.2021). Policies that include culture in rural development, however, lack an understanding of what this creative-rural ecology looks like or how it functions.<br/><br/>We aim to assess the strategies of cultural agents outside major city centres by asking how the spheres of arts and culture, the local economy and ‘the social’ interact in peripheral regions. Do these interactions contribute to sustainable (local) cultural/artistic milieus and to regional development? And how can such a contribution be demonstrated? Sustainability, here includes the durability of cultural ecologies themselves, i.e. cultural sustainability. We take the northern region of the Netherlands as our case study. This is one of peripheral regions in the country, spanning three provinces with one ‘urban’ centre, the city of Groningen (ca. 200.000 inhabitants). The region comprises ca. a quarter of the nation’s areal while only 10% of the population live there.<br/><br/>Our methodology combines a micro perspective, providing thick descriptions of the working realities of cultural agents in the region, analysing their collaborations with other agents in the cultural field and outside of the field of culture. To avoid hyperinstrumentalisation (Hadley and Gray 2017) we aim uncovering ‘emic’ (Beuving and De Vries 2014) value definitions of cultural agents rather than starting from the values defined by funders of art and culture. We combine these micro analyses with meso analysis through networks (Robins, 2012), mapping their development over four years, i.e. one cultural policy period. Currently, a preliminary round of data collection has been finished, allowing testing and finetuning our methodology.<br/><br/>Our conference paper therefore will demonstrate the value perspectives and networks of a part of the professional cultural networks in the city of Groningen. As our present data spans from 2017 to 2020, we can assess the impact of the COVID-19 pandemic on these creative networks. Our research takes a perspective in between current hyperlocal analyses of peripheral cultural agents and their values (e.g., Van der Vaart, 2019; Bell &amp; Orozco, 2020) and macroperspectives that try to assess social and economic impact on a more generic levels, such as is done with use of the Audience Spectrum (The Audience Agency, 2022) and the Culture Monitor (Boekman Foundation, 2022) in policy advice. It also links up with research on entrepreneurial ecosystems that looks at the interplay between micro/meso/macro perspectives as proposed by Srinivasan &amp; Venkatraman (2018; see also Goswami, Mitchell &amp; Bhagavatula 2018; Thompson, Purdy &amp; Venetresca 2018). Within art sociology, our research entails addressing the cultural sector from social systems theory, a theoretical lens seldom applied to the arts and cultural sectors.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score0.771

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.269
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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