Digital Humanities and Industry: identifying employment niches. A first overview on challenges and potential solutions
Notice bibliographique
Résumé
This presentation follows a workshop session that was held at the beginning of the DARIAH Annual Event 2023 in Budapest, HU. An informational booklet, designed by Tom Gheldof, details each of the DH Masters which we were able to investigate. It helped nourish our discussions during the workshop and thus is included as well. Postgraduate education in Digital Humanities (DH) has often led to careers for students in either the research or cultural heritage sector. Traditionally, the relationship between industry and Cultural Heritage institutions has typically been conceived as a collaboration to leverage funding mechanisms and develop projects to pursue a common interest, such as a technical innovation, or a knowledge sharing endeavour. The skills acquired within Digital Humanities (DH) taught postgraduate degrees are interdisciplinary and therefore transferable by their very nature, something that has been recognised among larger multinational companies. Indeed, a strong humanities education and familiarity with our methods can be an asset for business. Best practices for data stewardship and data management are similar whether one focuses on cultural heritage data, or business data, even if there are particularities. Yet among small and medium enterprises (SMEs) the proposition of employing a graduate from a field that is still in its relative infancy compared with more traditional disciplines can be seen as a risk. It therefore becomes necessary to identify the gaps, and indeed niches that rest between the current provision of training among DH scholars at a postgraduate (Masters) level, and the needs of the companies and future employers of DH graduates. Indeed, greater collaboration and fluidity between the cultural heritage and academic sphere, and that of business, via the DH alumni, can lead to greater outcomes for both, as these students can bring the best practices of both sectors in their future careers, thereby enriching both sectors and establishing interpersonal links (and the collaboration that grows from these links) via their networks. In light of this, it becomes necessary to foster internships that encourage and nurture experimental data spaces between cultural heritage, industry and academia.<br> <br> This paper will therefore share the conversation around the relationship between taught postgraduate DH programmes and industry by presenting the outcomes of a joint working-group workshop to be held on the periphery of the DARIAH Annual Event 2023. Furthermore, it will also include the results of preparatory surveys and interviews with directors and coordinators of various DH postgraduate programmes across Europe, specifically identifying the challenges and professional issues experienced by both DH Masters directors, and their alumni. This paper addresses the following key objectives: Identify the professional challenges and (new) employment opportunities of DH postgraduate taught programmes and their alumni at the European scale. Identify the benefits such a collaboration and exchange between the two sectors can bring. Identify opportunities and good practices of internships with industry and cultural heritage institutions, and their associated challenges. Strengthen the networking opportunities between master degrees, in such a way that expertise can be mapped at a pan-Infrastructural level to share and exchange trainers and trainees in the frame of Erasmus mobilities or Erasmus Mundus programmes. Our presentation will give visibility to these outputs, as a first step in a long-term effort to improve collaboration between industry, cultural heritage institutions and academia (specifically taught postgraduate DH degrees) in the frame of research infrastructures.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,004 | 0,000 |
| Communication savante | 0,005 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».