Intro to special issue
Notice bibliographique
Résumé
Intro to special issue How We Work With/in Culture Now: Reimagining Impact Assessment and GovernanceOver the last several decades, policymakers, funders, artists, and arts organizations alike have attempted to find ways to assess the impact of their investments in and support of the arts (e.g., Banks 2010; Dempwolf et al 2014; Essig 2018).Moreover, scholars and culture sector leaders have often noted that the sector tends to depend on a fairly narrow brand of research and evaluation practices to inform future directions.These approaches tend to be reliant on fundraising or marketing imperatives such as achieving a monetary goal for a revenue stream, or filling seats in performance halls, or to address the operational use of funding such as how many activities were conducted, and how many people were involved so that grant recipients (for example) might better account for public funds.But there is more to assessing impact than to report on outputs or aggregate numbers (e.g., Luka 2022).In 2020, while working with several policymakers and funders while having identified several scholars working on these kinds of questions including finding ways to ameliorate the dearth of qualitative impact assessment frameworks, Mass Culture decided to facilitate and develop a renewed level of collaboration across the arts community, arts funders, and academia to advance arts impact research in Canada.This initiative has resulted not just in the production of robust qualitative impact assessment frameworks but also a community of practice (e.g., Markham 2018; Wenger et al. 2002) that bridges between varied governance structures and practices to support cross-sectoral efforts to identify what the culture sector brings to society today.In 2020, Mass Culture convened a series of discussions that resulted in funding and participation commitments from several funder organizations (including Canada Council for the Arts, the Culture Statistics Working Group: Federal-Provincial-Territorial Culture and Heritage Table, Ontario Trillium Foundation, and the Toronto Arts Foundation) to support what became the Research in Residence: Arts' Civic Impact initiative (RinR) in 2021-22.i With the additional support of Mitacs ii funding, the project supported six graduate students as interns for RinR, followed by a successful request for 2022-23 for a Mitacs Postdoctoral Fellow to conduct further related research in cultural governance and creative labour.The RinR project's governance structure is illustrated below in Figure 1, including the six universities, six interns, and one postdoctoral fellow that participated (Carleton, Dalhousie, McGill, Toronto, and Winnipeg, and Emily Carr University of Art + Design's Aboriginal Gathering Place), as well as more than a dozen arts organizations.The researchers and their specific arts' civic impact area of focus were: Sydney Pickering, Indigenous Cultural Knowledge (Emily Carr); Emma Bugg, Climate and Sustainability (Dalhousie); Aaron Richmond, Health and Wellbeing (McGill); Shanice Bernicky, Diversity and Inclusion (Carleton); Audree Espada and Missy LeBlanc, Diversity and Inclusion (Winnipeg), and Laurence Deroin Dubuc, Creative Labour and Sustainability (Toronto).The latter Mitacs Postdoctoral Fellowship was held at University of Toronto Scarborough (UTSC), and hosted by Mass Culture, and their work appears in this special issue (Dubuc 2023).
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 | 0,003 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,005 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,010 | 0,007 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,006 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,333 | 0,258 |
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 ».