Exploring international collaboration and language dynamics in Digital Humanities: insights from co-authorship networks in canonical journals
Bibliographic record
Abstract
Purpose This paper presents a follow-on study that quantifies geolingual markers and their apparent connection with authorship collaboration patterns in canonical Digital Humanities (DH) journals. In particular, it seeks to detect patterns in authors' countries of work and languages in co-authorship networks. Design/methodology/approach Through an in-depth co-authorship network analysis, this study analysed bibliometric data from three canonical DH journals over a range of 52 years (1966–2017). The results are presented as visualised networks with centrality calculations. Findings The results suggest that while DH scholars may not collaborate as frequently as those in other disciplines, when they do so their collaborations tend to be more international than in many Science and Engineering, and Social Sciences disciplines. DH authors in some countries (e.g. Spain, Finland, Australia, Canada, and the UK) have the highest international co-author rates, while others have high national co-author rates but low international rates (e.g. Japan, the USA, and France). Originality/value This study is the first DH co-authorship network study that explores the apparent connection between language and collaboration patterns in DH. It contributes to ongoing debates about diversity, representation, and multilingualism in DH and academic publishing more widely.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.017 | 0.030 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".