Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1512.00443

Recommendations for the Technical Infrastructure for Standardized International Rights Statements

Valentine Charles·Esmé Cowles·Karen Estlund·Antoine Isaac·Tom Johnson·M. A. Matienzo·Patrick Peiffer·Richard J. Urban·Maarten Zeinstra
View original source ↗Read source document ↗Export metadata ↓

Abstract

This white paper is the product of a joint Digital Public Library of America (DPLA)-Europeana working group organized to develop minimum rights statement metadata standards for organizations that contribute to DPLA and Europeana. This white paper deals specifically with the technical infrastructure of a common namespace (rightsstatements.org) that hosts the rights statements to be used by (at minimum) the DPLA and Europeana. These recommendations for a common technical infrastructure for rights statements outline a simple, flexible, and extensible framework to host the rights statements at rightsstatements.org. This white paper specifically outlines the management of rights statements as linked open data. The rights statements are published according to Best Practices for Publishing RDF Vocabularies. They are encoded into dereferenceable URIs, express further information encoded in RDF, and link to existing vocabularies and standards. The rights statements adhere to expressions of existing rights vocabularies. Furthermore the paper reviews the publication and implementation to make the rights statements available through human-readable web pages augmented with machine-readable formats.

Explore related subjects

cs.DLcs.CY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Valentine Charles, Esmé Cowles, Karen Estlund, Antoine Isaac, Tom Johnson, M. A. Matienzo, Patrick Peiffer, Richard J. Urban, Maarten Zeinstra. 2022-08-24. Recommendations for the Technical Infrastructure for Standardized International Rights Statements. https://arxiv.org/abs/1512.00443

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

The illusion of neutrality in metric-based research evaluation

Venue prestige and citation counts are two widely used, albeit imperfect, signals of research quality. When the two signals conflict, evaluators must decide how much weight to assign each. Yet, it remains unknown how researchers across disciplines trade off these two signals when evaluating research outcome. To fill this gap, we surveyed 869 researchers using paired choices between hypothetical departmental hiring rules that assigned different weights to venue prestige and citation counts, asking which would produce better science. Among 795 respondents whose choices were largely internally consistent, choices placed nearly equal aggregate weight on the two signals. This apparent balance concealed substantial individual heterogeneity: nearly two in five respondents occupied the most venue-heavy or citation-heavy intervals. Moreover, respondents on average chose more citation-heavy rules than they believed their departments used in hiring. By revealing the subjective judgments that arise when research indicators conflict, our findings reinforce calls for greater caution when quantitative indicators are used to evaluate research.

cs.DL↗
arXiv2026

Scoring Grant Applications with Large Language Models

Purpose: Assessing grant applications is time-consuming and difficult, adding to the overall burden of academic peer review. Whilst funders are exploring whether AI can help, there is no published research into the accuracy of Large Language Models (LLMs) for scoring contemporary grants. Design/methodology/approach: This study investigates whether six open-weight LLMs (Gemma 3 1B/4B/12B/27B, DeepSeek R1 32B, Qwen 3 32B) can give useful scores for 2267 recent UK Economic and Social Research Council (ESRC), and Engineering and Physical Sciences Research Council (EPSRC) grant applications, comparing them with scores from the original reviewers and funding panel members. Findings: Although the LLM scores are individually inaccurate, when averaged and converted to ranks they correlate positively with expert average scores. The best performing LLM, Gemma 3 27B (10 iterations with varied prompts), had moderate rank correlations with average reviewer scores (mean rho=0.26). Gemma 3 27B's average correlation with individual reviewers was 0.19, which is lower than the inter-reviewer mean correlation of 0.24, suggesting that it scores are slightly weaker than individual reviewer scores. Gemma 3 27B had weak rank correlations with average panel member scores (mean rho=0.17), with lower average correlations with individual panellists (mean rho=0.14), which is substantially lower than the inter-panellist correlation (mean rho=0.38). Whilst the correlations seem too weak to replace expert review at the final panel stage, LLM scores might help with the initial reviewing state, such as by helping identify the weakest proposals for fast-track desk rejections, to replace one human reviewer, or for triangulation to check for bias.

cs.DL↗
arXiv2026

Open Science, Closed Models: How Funding Shapes AI in Science

How funding shapes AI engagement in science is poorly understood despite its structural importance. We analyze 104,226 scientific papers (2018-2025), linking funding acknowledgments to how each paper engages with foundation models: whether it extends a model (fine-tunes or builds on it), uses one without modification, or references models only peripherally. Three findings emerge. First, funding source is associated with the character of engagement: public funding is associated with higher open-weight model engage- ment; private-only funding selectively enables extension with no robust openness association; mixed funding shows nominally the highest open-weight engagement rates in four of the seven largest disciplines. Second, papers acknowledging industry cloud credits are less likely to use open-weight models, consistent with credit programs steering research toward closed models. Third, industry collaboration carries an independent extension premium consistent with internal corporate resources flowing through coauthorship channels invisible to acknowledgment-based measures. This nexus concentrates in Computer Science and Global North collaborations; Global South research is largely excluded. As public funding contracts and industry compute provision expands, scientific AI work shifts toward closed proprietary infrastructure.

cs.DL↗