The study, published in the journal Discover Informatics, examined eight Ghanaian universities with operational institutional repositories, including the University of Ghana, Kwame Nkrumah University of Science and Technology, the University of Cape Coast, the University of Education Winneba, the University for Development Studies, Ashesi University, the Ghana Institute of Journalism, and the University of Professional Studies Accra. Led by De-Graft Johnson Dei and Karim Awudu, the research team surveyed 120 respondents drawn from ICT personnel, repository managers, academic librarians, systems librarians, and faculty members, then followed up with in-depth interviews with twelve senior experts. The design was a sequential explanatory mixed-methods approach, meaning the survey results shaped the questions asked in later interviews, and the two strands of evidence were triangulated during interpretation.

The headline finding is sobering. Forty percent of respondents said their institutions had no digital rights management mechanism in place at all. Access control was the most commonly implemented practice, appearing at 44.2 percent of institutions, while advanced techniques such as digital watermarking and encryption were implemented at only 9.2 percent. Usage tracking, copyright auditing, and automated licensing enforcement were similarly rare. Interview participants described repositories that upload content without defined rights policies, verify permissions manually, and have no idea who downloads what or where it ends up. One participant at the University for Development Studies put it bluntly: access matters more than protection. Another observed that if content is on the internet, people assume it is free to use, a presumption that puts author rights at risk.

Against this backdrop, blockchain technology has been proposed as a transformative tool. A blockchain is a distributed ledger in which transactions are recorded in cryptographically verified, time-stamped blocks linked to their predecessors, maintained across a network of nodes rather than a single central authority. Because no single party controls the record, entries cannot be quietly altered, and every change leaves a permanent, auditable trail. For digital rights management, this architecture offers several distinct functions: authorship verification through immutable timestamps and digital signatures, provenance tracking across the scholarly lifecycle, content integrity through cryptographic verification, and automated rights enforcement through smart contracts, which are self-executing programs that apply licensing terms without continuous administrative intervention.

The researchers grounded their analysis in two complementary theoretical lenses. The Technology-Organization-Environment framework, developed by Tornatzky and Fleischer, examines how technological features, organizational capacity, and environmental conditions such as national policy shape whether institutions adopt an innovation. Diffusion of Innovations theory, articulated by Everett Rogers, explains how individual stakeholders perceive new ideas through the attributes of relative advantage, compatibility, complexity, trialability, and observability. Together, the frameworks allowed the team to assess both the structural readiness of Ghanaian universities and the attitudes of the people who would actually operate blockchain-enabled systems.

Awareness of blockchain turned out to be uneven. Overall, 62.5 percent of respondents had heard of the technology, but mean perceived understanding was low at 2.31 on a five-point scale. ICT personnel were far ahead, with 86.7 percent awareness and a mean understanding score of 3.82, while faculty members lagged at 33.3 percent awareness and a mean of 1.74. Interviews revealed persistent conceptual confusion, with some participants equating blockchain entirely with Bitcoin and others describing it as futuristic but unproven in a library context. Several respondents expressed a genuine appetite for training, noting that interest exists but workshops and webinars do not. According to Diffusion of Innovations theory, these conditions mark an early adoption stage characterized by low observability and high perceived complexity, yet also a latent receptiveness that targeted demonstration could convert into adoption.

Institutional readiness fared worse. The overall preparedness score was 2.25, rated as poor, and only 18.3 percent of respondents believed their infrastructure could support blockchain. A striking 81.7 percent said their digital library strategies made no mention of the technology. Funding was the dominant constraint, cited by 74.2 percent, followed by technical complexity at 74.2 percent, shortage of qualified staff at 72.5 percent, and absence of legal and regulatory frameworks at 68.3 percent. Participants described server capacity too limited to host current systems, ICT plans with no blockchain reference whatsoever, and donor projects that fund basic digital libraries but not cutting-edge infrastructure. In northern Ghana, unreliable internet and electricity compounded the problem, cited by 54.2 percent of respondents.

Yet the appetite for blockchain’s benefits was remarkably strong. Tamper-proof authorship verification topped the list, endorsed by 78.3 percent of respondents with a mean rating of 4.21, followed by improved content authenticity and integrity at 74.2 percent and transparent audit trails at 71.7 percent. Smart contract automation of copyright enforcement drew support from 66.7 percent, blockchain timestamping from 61.7 percent, and decentralized data control from 63.3 percent. Librarians interviewed for the study described repeated incidents of downloaded content being renamed and shared without credit, and saw in blockchain a way to safeguard the uniqueness of their researchers’ work. Others noted that precise access tracking could transform impact monitoring, and that smart contracts could enforce Creative Commons licenses without manual policing.

On strategy, stakeholders favored cautious, low-risk pathways. Staff training and capacity building was the most endorsed approach at 79.2 percent, followed by pilot blockchain repository modules at 76.7 percent and phased implementation at 74.2 percent. Open-source platforms such as Hyperledger attracted 72.5 percent support, reflecting a preference for affordable, flexible tools over commercial systems. Multidisciplinary task forces spanning librarians, attorneys, and IT professionals, donor and government funding, and regional consortia all received strong ratings, while alignment with national education policy goals ranked lowest at 60.8 percent, exposing a strategic disconnect between institutional action and frameworks set by the Ministry of Education and the Ghana Tertiary Education Commission.

The study concludes that blockchain-enabled digital rights management in Ghanaian repositories is a promising but still emerging innovation, transformative in potential yet constrained by infrastructure, funding, expertise, and policy. The authors recommend phased implementation, pilot projects beginning perhaps with postgraduate theses, open-source tools, interdisciplinary collaboration, and sustained investment in digital infrastructure and human capacity. They also caution that the findings rest on self-reported perceptions from eight universities and do not evaluate operational blockchain systems, leaving room for future work on prototypes, longitudinal adoption studies, and integration with technologies such as big data analytics and large language models. For now, the message is clear: Ghana’s repositories hold valuable scholarship that is largely unprotected, and while blockchain is no silver bullet, stakeholders across the country’s universities are ready to explore whether a decentralized ledger can finally give their research the trust and security it deserves.

Subject of Research: Blockchain adoption for digital rights management in Ghanaian university institutional repositories

Article Title: Evaluating blockchain adoption for digital rights management in institutional repositories

Article References: Johnson Dei, D.-G., & Awudu, K. (2026). Evaluating blockchain adoption for digital rights management in institutional repositories. Discover Informatics, 1(1), Article 5. https://doi.org/10.1007/s44564-026-00008-z

Image Credits: AI Generated

DOI: 10.1007/s44564-026-00008-z

Keywords: blockchain technology, digital rights management, institutional repositories, Ghana, higher education, scholarly communication, smart contracts, copyright protection, digital preservation, open access, TOE framework, Diffusion of Innovations

As large language models now produce everything from open-domain articles to executable code and step-by-step mathematical derivations, the need to verify authenticity, attribute sources, and protect copyright has grown urgent. Text watermarking has emerged as a leading countermeasure, typically by injecting small statistical biases into a model’s token-selection process during generation. The most influential example is the KGW scheme, which pseudorandomly partitions the vocabulary into “green” and “red” token lists and nudges the model toward green tokens, leaving behind a statistical footprint that a detector can later pick up. Google’s SynthID-Text has since demonstrated that such decoding-based watermarking can work at production scale.

The trouble, the researchers explain, is that most existing schemes assume a stable generation pattern. They rely on fixed or heuristic entropy thresholds to decide where the watermark should be applied. Token entropy—a measure of how uncertain the model is at a given step—matters enormously: in high-entropy contexts, such as creative prose, the model has many plausible choices, and biasing one direction barely changes the output. In low-entropy contexts, such as the syntax of a Python function or a symbolic derivation, the “correct” next token is nearly deterministic, and any perturbation risks breaking the code or corrupting the logic. A single fixed threshold inevitably misjudges one regime or the other, producing either redundant watermark injection in sensitive regions or noisy, unreliable detection statistics. Earlier attempts to adapt—SWEET, which applies strong bias only above a static entropy threshold, and CATMARK, which updates cluster centers online at considerable computational cost—only partially solve the problem.

GAMark’s central insight is that the semantic states traversed during generation are not uniform. The team draws on the manifold hypothesis: the high-dimensional logit distributions that a language model produces across diverse tasks do not fill their ambient space but cluster on a lower-dimensional structure corresponding to distinct latent semantic states—prose generation, code writing, formal reasoning. GAMark exploits this structure through a two-phase paradigm of offline semantic modeling and frozen online inference.

In the offline phase, the researchers collect logit-level representations from the target models and reduce them using Incremental Principal Component Analysis to a 50-dimensional subspace. They then fit a Gaussian Mixture Model with ten components, each capturing one characteristic semantic mode. In the online phase, the GMM parameters remain frozen: for each token position during generation, the current logit vector is projected and assigned soft, Bayesian posterior-weighted memberships across the semantic states. The adaptive entropy threshold is then synthesized by aggregating over those memberships, allowing the threshold to shift smoothly as the model transitions between, say, natural-language commentary and a code keyword. Only when the token’s entropy exceeds this dynamically computed threshold does the watermark gate open, biasing the green-listed tokens with strength δ while leaving deterministic tokens untouched.

Detection is handled with a matching procedure. Because the model parameters and the GMM are frozen, a detector can reconstruct the same semantic states and thresholds from the raw logits of a suspect text, recover the validity mask indicating which tokens were actually eligible for watermarking, and then compute an entropy-weighted Z-score. The weight assigned to each token scales with the excess of its entropy over the threshold, raising the exponent η to either 0 or 1. The resulting statistic follows, by the Central Limit Theorem, an approximate normal distribution under the null hypothesis, yielding a one-sided hypothesis test: a Z-score above 4.0 corresponds to a false-positive probability below 3.17 × 10⁻⁵. This weighting scheme suppresses the dilution caused by the many low-entropy, never-watermarked tokens that code and math text contain.

The experimental evaluation spanned two models—Qwen2.5-7B-Instruct and LLaMA3.1-8B-Instruct—across four benchmarks chosen for their diverse entropy profiles: C4 (RealNews) for high-entropy open-domain text, HumanEval’s 164 Python problems for functionality under strict syntax, MBPP for concise low-entropy code synthesis, and MATH-500 for interleaved natural-language and symbolic reasoning. Five baselines were compared under unified settings with a green-list ratio of 0.5 and a base logit bias of 2.0: KGW, SWEET, EWD, SynthID, and CATMARK.

The results show consistent gains on both sides of the quality–detectability trade-off. On C4, GAMark achieved a perplexity of 5.96, better than KGW (7.15), SWEET (6.65), and SynthID (6.10), indicating that the adaptive thresholds leave the generated prose nearly indistinguishable from unwatermarked output. On HumanEval, the contrast was starker: CATMARK had to suppress watermark signals to hold its Pass@1 correctness at 74.4%, paying for it with an AUROC of only 65.09% and a true positive rate of 15.85%. GAMark, by contrast, reached the highest Pass@1 of 76.2% while achieving 89.30% AUROC and 81.70% TPR—more than five times the detection sensitivity of CATMARK. On MATH-500, where entropy regimes switch within a single response, GAMark recorded 64.4% Pass@1 with 97.23% AUROC on Qwen2.5-7B-Instruct, and an AUROC of 99.27% with 97.60% TPR on LLaMA3.1-8B-Instruct.

Robustness testing under aggressive semantic attacks reinforced the picture. In a back-translation attack—routing text from English to Chinese and back via the DeepSeek API—the KGW baseline’s AUC collapsed from 0.812 to 0.650, while GAMark held at 0.910. Under full paraphrasing by Qwen2.5-7B-Instruct, most baselines shifted sharply toward false negatives, but GAMark’s AUC declined only from 0.972 to 0.880, well ahead of the second-best method, EWD, at 0.820. The researchers attribute this resilience to the fact that the watermark concentrates in semantically flexible, high-entropy regions that survive rewriting, rather than in brittle structural tokens.

Sensitivity analyses pinpointed the hyperparameters that matter. With too few GMM clusters, coarse semantic modeling fails to shield low-entropy regions; performance peaked at K = 10, beyond which quality declined slightly. The threshold scaling coefficient α governs the trade-off: α = 0.5 yields near-perfect detection (99.2% TPR on HumanEval) but crashes Pass@1 to 56.5% by mistakenly watermarking deterministic tokens, while α = 1.5 restores output quality but weakens detection below 40% TPR. The default α = 1.0 balances both, delivering 76.2% Pass@1 and 81.7% TPR on HumanEval.

Efficiency results were equally notable. Because all heavy manifold modeling is shifted offline, online inference requires only lightweight matrix projections and GMM posterior estimation. GAMark generated text at 35.92 tokens per second on HumanEval, outpacing CATMARK’s 33.13 tokens per second and even slightly exceeding the static SWEET baseline, with only 3.8% overhead compared to CATMARK’s 9.4%. An ablation study confirmed the necessity of each component: removing GMM clustering dropped Pass@1 by 7.7 points on HumanEval, and removing entropy-weighted detection cut AUROC by 7.15 points, showing that semantic state modeling preserves functionality while the weighted statistic filters the noise that unwatermarked low-entropy tokens would otherwise introduce.

The team concludes that GAMark offers a unified, practical answer for watermarking heterogeneous, cross-task generation environments—precisely the settings where modern LLMs are deployed. The researchers plan to extend the framework to more challenging adversarial settings and further optimize efficiency for large-scale deployment, work that could prove consequential as regulators, platforms, and publishers increasingly demand verifiable provenance for AI-generated content.