Reciprocal Collaboration: how lessons from convergence in GLAMs can enhance interdisciplinary AI research
- DOI
- 10.48550/arxiv.2609.26023
- Published
- 2026
- Container
- Not recorded
- Publisher
- arXiv
- Open access
- yes
Credibility signals
limited evidence Score 43/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.48550/arxiv.2609.26023,
title = {Reciprocal Collaboration: how lessons from convergence in GLAMs can enhance interdisciplinary AI research},
author = {Cushing, Amber L. and Little, Suzanne and Osti, Giulia},
year = {2026},
doi = {10.48550/arxiv.2609.26023},
url = {https://doi.org/10.48550/arxiv.2609.26023}
}RIS
TY - JOUR TI - Reciprocal Collaboration: how lessons from convergence in GLAMs can enhance interdisciplinary AI research AU - Cushing, Amber L. AU - Little, Suzanne AU - Osti, Giulia PY - 2026 DO - 10.48550/arxiv.2609.26023 UR - https://doi.org/10.48550/arxiv.2609.26023 ER -
APA
L., C. A., Suzanne, L., & Giulia, O. (2026). Reciprocal Collaboration: how lessons from convergence in GLAMs can enhance interdisciplinary AI research. https://doi.org/10.48550/arxiv.2609.26023
Source records
- datacite · retrieved 2026-09-27T07:06:00.181Z