Digital Humanities and Industry: identifying employment niches. A first overview on challenges and potential solutions
Bibliographic record
Abstract
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. 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.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 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".