“Short glossary containing the main concepts related to Rights Retention and Open Licensing developed by Project Retain led by SPARC Europe. This is part of the Knowledge Rights 21 programme.”
“To help inform the special education research community, these briefs feature information on prominent open science practices. Content comes from our series of short articles in the DR newsletter, Focus on Research, as well as additional content developed by DR members.
Open science is an umbrella terms that refers to practices aiming to make all stages of science more open and transparent. Although some have argued that open science can make research more trustworthy, impactful, and efficient in special education (Cook et al., 2018), there is a lack of clarity in the field about what open-science practices are, their primary benefits and potential obstacles, and how to access resources for implementing them. In this brief, we discuss arguably the best-known aspect of open science: open access….”
Abstract: This paper examines ‘open’ AI in the context of recent attention to open and open source AI systems. We find that the terms ‘open’ and ‘open source’ are used in confusing and diverse ways, often constituting more aspiration or marketing than technical descriptor, and frequently blending concepts from both open source software and open science. This complicates an already complex landscape, in which there is currently no agreed on definition of ‘open’ in the context of AI, and as such the term is being applied to widely divergent offerings with little reference to a stable descriptor.
So, what exactly is ‘open’ about ‘open’ AI, and what does ‘open’ AI enable? To better answer these questions we begin this paper by looking at the various resources required to create and deploy AI systems, alongside the components that comprise these systems. We do this with an eye to which of these can, or cannot, be made open to scrutiny, reuse, and extension. What does ‘open’ mean in practice, and what are its limits in the context of AI? We find that while a handful of maximally open AI systems exist, which offer intentional and extensive transparency, reusability, and extensibility– the resources needed to build AI from scratch, and to deploy large AI systems at scale, remain ‘closed’—available only to those with significant (almost always corporate) resources. From here, we zoom out and examine the history of open source, its cleave from free software in the mid 1990s, and the contested processes by which open source has been incorporated into, and instrumented by, large tech corporations. As a current day example of the overbroad and ill-defined use of the term by tech companies, we look at ‘open’ in the context of OpenAI the company. We trace its moves from a humanity-focused nonprofit to a for-profit partnered with Microsoft, and its shifting position on ‘open’ AI. Finally, we examine the current discourse around ‘open’ AI–looking at how the term and the (mis)understandings about what ‘open’ enables are being deployed to shape the public’s and policymakers’ understanding about AI, its capabilities, and the power of the AI industry. In particular, we examine the arguments being made for and against ‘open’ and open source AI, who’s making them, and how they are being deployed in the debate over AI regulation.
Taken together, we find that ‘open’ AI can, in its more maximal instantiations, provide transparency, reusability, and extensibility that can enable third parties to deploy and build on top of powerful off-the-shelf AI models. These maximalist forms of ‘open’ AI can also allow some forms of auditing and oversight. But even the most open of ‘open’ AI systems do not, on their own, ensure democratic access to or meaningful competition in AI, nor does openness alone solve the problem of oversight and scrutiny. While we recognize that there is a vibrant community of earnest contributors building and contributing to ‘open’ AI efforts in the name of expanding access and insight, we also find that marketing around openness and investment in (somewhat) open AI systems is being leveraged by powerful companies to bolster their positions in the face of growing interest in AI regulation. And that some companies have moved to embrace ‘open’ AI as a mechanism to entrench dominance, using the rhetoric of ‘open’ AI to expand market power while investing in ‘open’ AI efforts in ways that allow them to set standards of development while benefiting from the free labor of open source contributors.
From Google’s English: Abstract: Objective : to review the terminologies and applications of the Open Science taxonomy in order to build a more comprehensive version, which represents the knowledge surrounding the theme, in accordance with the current scenario of scientific communication and with the recommendations of the United Nations Organization for the Education, Science and Culture (Unesco).
Method : this is an exploratory research with a deductive approach. The first stage was the review of taxonomies, with 12 researchers who met weekly for conceptual and epistemological discussions related to Open Science, and methodological and procedural definitions for carrying out the study.
Results : as a result of the analyses, a taxonomy was developed to be evaluated by the specialists. For this, a questionnaire with open questions was sent, about each main axis of the taxonomy, to 68 specialists. 21 answers were obtained that cooperated with the modeling and exposition of the terms for the new taxonomy. The taxonomy that came out of this review process has 10 main-level facets and a total of 96 labels.
Conclusions: the specialists’ perception brought to light a congruent panorama with the recommendations of Unesco and the current scenario of Open Science.
Abstract: This report outlines IOI’s initial attempt towards a framework for understanding open infrastructure for research and scholarship. For this report, we examined a body of literature that includes works across the fields of anthropology, scholarly communications, international development studies, science and technology studies, and infrastructure studies.
“Previously, we shared a draft report on our preliminary investigation into defining open scholarly infrastructure and invited your comments and questions. We now share the final version of this report. IOI strives to build on the efforts of others working to improve funding and resourcing for the open infrastructure on which scholarly research relies. As an organization, one of our aims is to enhance our shared understanding of infrastructure in scientific research and scholarly communication. We believe that a more profound understanding of this area will have substantial consequences for how organizations providing research and scholarship services are supported and engaged with. This report serves as an initial stage in a continuous process that the organization intends to refine and develop over time. In this preliminary investigation, we first examined a body of literature that includes works across the fields of anthropology, scholarly communications, international development studies, science and technology studies, and infrastructure studies. We synthesized the characteristics and dimensions defined in the literature we reviewed to produce the initial draft, which we then opened for public comments for two weeks. Our research team then synthesized the feedback from the community to create the final report that we hope gives more divergent perspectives that have enriched our understanding of open infrastructure in research and scholarship….”
“What does “open” ? mean today? What should it mean? What has changed since 2015, when the Open Definition was last updated?
We at Open Knowledge are preparing another round of consultations on updating the “Open Definition”. We will have a face-to-face session in Spanish during RightsCon to ensure the voices of the Latin American communities gathered in Costa Rica are heard and incorporated to the review process. It will be a practical session to write collectively….”
“One week ago at MozFest, we began the process of rethinking and updating the Open Definition for today’s challenges and contexts. …Key Takeaways: Diversity – It was recognised that the original Open Definition process was mostly carried out by people with a fairly high-level profile, but little diversified. Governance – It is necessary to design a new governance model for the Open Definition to seek an even greater consensus than before. There is a need to actively and radically include people from other origins, races, genders, classes, etc., and in a way that everyone feels a constituent part of the process. Misuse – One of the biggest problems when it comes to open content today is the misuse and abuse of the word open, used to describe technologies and contexts that actually do not satisfy any of the criteria defined by the Open Definition. Participants mentioned the need for mechanisms for reporting misuses, or how the definition could have a more supervisory/validating role. Ethics – There was a discussion about the term “for any purpose” which, according to the current definition, is an essential part of what makes content open. Some arguments revolved around the concepts of “responsible use” (like in Responsible AI Licences), or “do no evil”. Universality – There were also debates about the universality of the concept. Some argued that there should be a single generalised definition, while others pointed out the need to make the definition always dynamic and context-related. Language – Many pointed out that the Open Definition should abandon jargon, legal, economic and technical vocabularies to adopt a more accessible and easy-to-understand language, especially for those who are not familiar with the concept.
Considering the above, we at the Open Knowledge Foundation are happy to announce that: We are absorbing the feedback and organising ourselves to take the first formal step: proposing a governance model to guide discussions in the coming months. We are reactivating the official Open Definition discussion forum, where the past conversations took place. Anyone who would like to contribute is welcome to join. We are slowly revamping and editing the Open Definition website (open to contributions via GitHub) and preparing it for the upcoming discussions….”
“There’s a lot of different terminology around open access, particularly around various levels of open access. I thought it might be helpful to aggregate some of the disparate information into one source on the TOPS Github, which is below! This is sourced from Open Book Publishers, Researcher.Life, and Taylor & Francis.There are many kinds of open access, but they broadly fit into three categories: libre, which is open access that allows content to be free to read and generally, there are no barriers for reuse, gratis, which is open access that allows content to be free to read, but has barriers for reuse, and then there’s one level (black) that fits into neither libre nor gratis….”
“Scholarly Communication” is a frequent topic of both the professional and research literature of Library and Information Science (LIS). Despite efforts by individuals (e.g. Borgman, 1989) and organizations such as the Association of College and Research Libraries (ACRL) to define the term, multiple understandings of it remain. Discussions of scholarly communication infrequently offer a definition or explanation of its parameters, making it difficult for readers to form a comprehensive understanding of scholarly communication and associated phenomena.
This project uses the evolutionary concept analysis (ECA) method developed by nursing scholar, Beth L. Rodgers, to explore “Scholarly Communication” as employed in the literature of LIS. As the purpose of ECA is not to arrive at “the” definition of a term but rather exploring its utilization within a specific context, it is an ideal approach to expand our understanding of SC as used in LIS research.
“Scholarly Communication” as employed in the LIS literature does not refer to a single phenomenon or idea, but rather is a concept with several dimensions and sub-dimensions with distinct, but overlapping, significance.
The concept analysis (CA) method calls for review of a named concept, i.e. verbatim. Therefore, the items included in the data set must include the phrase “scholarly communication”. Items using alternate terminology were excluded from analysis.
The model of scholarly communication presented in this paper provides language to operationalize the concept.
LIS lacks a nuanced understanding of “scholarly communication” as used in the LIS literature. This paper offers a model to further the field’s collective understanding of the term and support operationalization for future research projects.
Abstract: The technical complexity and functionality of computer programs have made it difficult for courts to apply conventional copyright concepts, such as the idea/expression distinction, in the software copyright case law. This has created fertile ground for significant misconceptions. In this paper, we identify fourteen such misconceptions that arose during the lengthy course of the Google v. Oracle litigation. Most of these misconceptions concern application programming interfaces (APIs). We explain why these misconceptions were strategically significant in Oracle’s lawsuit, rebut them, and urge lawyers and computer scientists involved in software copyright litigation to adopt and insist on the use of terminology that is technically sound and unlikely to perpetuate these misconceptions.
The Open Definition makes precise the meaning of “open” with respect to knowledge, promoting a robust commons in which anyone may participate, and interoperability is maximized.
Summary: Knowledge is open if anyone is free to access, use, modify, and share it — subject, at most, to measures that preserve provenance and openness.
This essential meaning matches that of “open” with respect to software as in the Open Source Definition and is synonymous with “free” or “libre” as in the Free Software Definition and Definition of Free Cultural Works….”
“The Open Definition (https://opendefinition.org/od/2.1/en/) is one of the most historically important collaborative works for the open movement. However, over the years and due to the emergence of new technologies, identities and enclosures, we at Open Knowledge feel that this work needs to be expanded, including more voices, diversity, and cultural contexts. We want to invite the Mozilla communities to a hands-on session to create a document about what, why and how the Open Definition should be reviewed. Join us in thinking what “open” means today!…”
“The Open Definition initiative was a collaborative process led by the Open Knowledge Foundation more than a decade ago that created a consensus among experts by defining openness in relation to data and content, in a collaborative, open process with volunteer leading experts in the field, who did a remarkable job.
It specified what licences for such material may and may not stipulate to be considered open. It turned out to be one of the most historically important collaborative works for the open movement.
It was mission accomplished at some point, and there was no pressing need to review it – just to maintain it and observe how the open knowledge ecosystem was adopting it. It got translated into 41 languages by volunteers, it made it to Wikipedia and it influenced state and municipal policies, academia, and beyond.
However, technology and policy have profoundly changed since the 2.1 version, its last update from 2016. Since then, technology, society and conversations around what should be open and shared have expanded in geography and complexity….”
The term open has become a familiar part of library and education practice and discourse, with open source software being a common referent. However, the conditions surrounding the emergence of the open source movement are not well understood within librarianship. After identifying capitalism and neoliberalism as structures that shape library and open practice, this article contextualizes the term open by delineating the discursive struggle within the free software movement that led to the emergence of the open source movement. An understanding of the genealogy of open can lend clarity to many of the contradictions that have been grappled with in the literature, such as what open means, whether it supports social justice aims, and its relation to neoliberal and capitalist structures. The article concludes by inquiring into how librarianship and open can reframe practices that are typically oriented toward mitigation and survival to encompass an orientation toward life and flourishing.