Open linguistic data, governance and data activism

Introduction

This module aims to investigate how the concepts of open data and open governance trigger open linguistic actions, practices and data activism. In particular, the first unit introduces the data governance frameworks of Openness and the CARE (Collective benefit, Authority to control, Responsibility and Ethics) principles. The second unit, through a discussion on the concept of language variety, places a particular emphasis on the value of indigenous and endangered languages, while the last unit offers a new perspective of language activism by introducing the concept of data activism.

This module, through the introduction of two state-of-the-art frameworks on data governance, namely Openness and CARE principles, aims to interrelate the concept of linguistic variety with that of data activism. The final result is a new approach to language activism through the prism of data governance and particularly Openness and CARE principles.

Katerina Zourou, Stefania Oikonomou

On completion of this module participants will be able to:

  • understand the role and the meaning of Openness and CARE principles frameworks;
  • understand the complexity of defining language variety;
  • identify the value of indigenous and endangered languages;
  • understand the contribution of data activism, allowing them to act towards the protection of language variety.

This module is open to any interested person eager to learn more on open linguistic data, governance and data activism. It particularly serves training purposes of:

  • university students in education and the learning sciences: this module will critically contribute to students’ pursuing bachelor and/or master degrees in any education-related field. In a multicultural educational environment, buttom-up actions in parallel with learning resources are deemed as necessary for safeguarding multilingualism;
  • civil society organisations: this module will offer individuals working at the civil society sector a concrete view over the topics of data governance, by emphasising on Openness and CARE principles  and language variety. The aim is to stimulate them towards undertaking actions in favour of language vitality and sustainability.
  • Unit 1: It focuses on Openness and CARE principles. The objective of this unit is for learners to become familiarised with these key definitions and to develop their skills on identifying cases of Openness and CARE principles. 
  • Unit 2: It introduces the theoretical framework on linguistic diversity, by bringing  emphasis on Indigenous and Endangered languages.
  • Unit 3: It deals with the central question of how language activism can be realised through data activism.

2 hours

The module’s main materials are based on a variety of resources:

  • Studies and reports issued by distinguished authors and international organisations on data governance, language variety and language activism. 
  • Explanatory videos with and video-lectures.
  • Materials provided by international organisations and Higher Educational Institutions through their official web pages.

Unit 1: Openness and CARE principles

In a world of complex social structures and various centres of knowledge production (e.g., universities, knowledge communities, etc.), digital networking has become a game-changer, radically transforming the way knowledge is generated and diffused. In this context, new and participatory information channels and flows have been created, offering people access to newly produced information and knowledge. Additionally, digital technologies have fostered an unprecedented accumulation and systematisation of data that was never witnessed before (Pentland, 2008). 

Therefore, regarding the above context, this unit will focus on issues related to two newly emerged frameworks of data governance; namely, Openness and CARE principles. To achieve the objectives of this module as well as the general objective of the BOLD project,  it  will examine the role of data by placing a particular emphasis on the following key questions: 

  • What is the ownership status of collected data? 
  • Who has the right to use the data? 
  • At what cost can someone use a dataset? 
  • What are the possible consequences of making data freely available? 

Hence, the learning objectives of this unit for learners are to:

  • become familiarised with the key definitions; 
  • to develop skills on identifying cases of Openness and CARE principles.

1.1 Openness

1.1.1 Introduction: Intellectual work and copyright

By delving into European history, we identify two critical points with regards to intellectual production before and after which a radical shift is observed. The first point is the invention of printing in 1434 by Gutenberg that totally changed the way books are produced and accessed by readers. The second point, coming as a logical consequence of the first, is the Statute of Anne (also known as the Copyright Act 1710), a legal act issued by the British Parliament in 1710 which was the first in history to mention Copyright as a legally regulated activity by the government and courts. 

What Copyright (symbol: ©) means, according to the Oxford Dictionary, is the right of a person or an organisation (henceforth: the originator) to legally print, (re-)publish, perform, (re-adapt) etc. an intellectual work. According to the World Intellectual Property Organization (WIPO), an intellectual work is defined as ‘the creations of the mind, such as inventions; literary and artistic works; designs; and symbols, names and images used in commerce’. In other words, an intellectual work is the intellectual property of its originator. Hence, Copyright refers to a set of legal regulations strictly rendering an intellectual work an absolute property of its originator. 

On an international level, Copyright is regulated by the Berne Convention (signed in 1886), managed and supervised by the WIPO [see here a synopsis of the Convention]. The convention outlines specific restrictions for users or consumers of an intellectual work in terms of having access, modification and sharing permissions without the legal approval of its originator.

1.1.2 The ‘Open’ definition

In contrast to the strict relation between an intellectual work and its originator, regulated by the Copyright, the concept of ‘Openness’ aims to define a more loose relation between the two. Specifically, according to the Open Knowledge Foundation [see here], Open is defined as the kind of knowledge, thus any kind of intellectual work related to it, that anyone is allowed to freely have access to and (re-use) it. This means for users that there are no charges for (re-)using the work and sharing it. The four principles constituting the Open definition must be fulfilled in order for an intellectual work to be considered as ‘Open: 

Prerequisites for Openness

According to the Open definition as provided by the Open Knowledge Foundation (n.d), for a work to be ‘Open’, it must satisfy the following requirement1: 

  1. Open licence: “The work must be in the public domain or provided under an open licence2 [see section 1.1.3 for a detailed definition of the term]. Any additional terms accompanying the work (such as a terms of use, or patents held by the licensor) must not contradict the work’s public domain status or terms of the licence”.  
  2. Access: “The work must be provided as a whole and at no more than a reasonable one-time reproduction cost, and should be downloadable via the Internet without charge. Any additional information necessary for licence compliance (such as names of contributors required for compliance with attribution requirements) must also accompany the work”. 
  3. Machine readability: “The work must be provided in a form readily processable by a computer and where the individual elements of the work can be easily accessed and modified”. [See also the FAIR principles that require data to be findable, accessible, interoperable and reusable.]
  4. Open format: “The work must be provided in an open format. An open format is one which places no restrictions, monetary or otherwise, upon its use and can be fully processed with at least one free/libre/open-source software tool”.3

The following schema depicts the four dimensions of Open definition.

It should be noticed that the conditions under which an Open work is publicly available must not be contradictory to any of the above prerequisites to the Open definition. Otherwise, the work is not ‘Open’.

Finally, regarding the definition of Openness, the following picture (made by Katja Mayer) summarises its several paradigms-fields of knowledge.

The Open paradigms CC BY 4.0 Katja Mayer

1 All definitions are quoted verbatim.
2 According to the Open Knowledge Foundation (n.d), “the term public domain denotes the absence of copyright and similar restrictions, whether by default or waiver of all such conditions”.
3 According to openscource.com, “Open source software is software with source code that anyone can inspect, modify, and enhance” while as “Source code” is defined “the part of software that […] computer programmers can manipulate to change how a piece of software […] works.

1.1.3 Open Licences

Technically, what distinguishes an open work from a copyright-protected one is its licence, namely the legal terms under which it is publicly provided (The Open Knowledge Foundation, see here). A licence contains the mixture of the conditions and permissions under which a work is available. 

Those conditions are divided in two main categories;

  • obligatory ones, touching upon the core of the Open definition, and 
  • optional ones (i.e. non-obligatory conditions). 

For a licence to be characterised as open, it must be subject to all obligatory conditions. [Check here the catalogue of  compatible licences, and here the catalogue of the non-compatible ones.]

The obligatory conditions are introduced as follows, accompanied with the definitions provided by the  Open Knowledge Foundation(n.d.):4

Obligatory Open conditions 

  1. Use: “The licence must allow free use of the licensed work”. 
  2. Redistribution: “The licence must allow redistribution of the licensed work, including sale, whether on its own or as part of a collection made from works from different sources”. 
  3. Modification: “The licence must allow the creation of derivatives of the licensed work and allow the distribution of such derivatives under the same terms of the original licensed work”. 
  4. Separation: “The licence must allow any part of the work to be freely used, distributed, or modified separately from any other part of the work or from any collection of works in which it was originally distributed. All parties who receive any distribution of any part of a work within the terms of the original licence should have the same rights as those that are granted in conjunction with the original work”. 
  5. Compilation: “The licence must allow the licensed work to be distributed along with other distinct works without placing restrictions on these other works”. 
  6. Non-discrimination: “The licence must not discriminate against any person or group”. 
  7. Propagation: “The rights attached to the work must apply to all to whom it is redistributed without the need to agree to any additional legal terms”.
  8. Application “The licence must allow use, redistribution, modification, and compilation for any purpose. The licence must not restrict anyone from making use of the work in a specific field of endeavour”. 
  9. No charge: “The licence must not impose any fee arrangement, royalty, or other compensation or monetary remuneration as part of its conditions”. 

The non-obligatory conditions are the following, as they defined by the Open Knowledge Foundation (n.d.):5

Non-Obligatory Open conditions

  1. Attribution: “The licence may require distributions of the work to include attribution of contributors, rights holders, sponsors, and creators as long as any such prescriptions are not onerous”. 
  2. Integrity: “The licence may require that modified versions of a licensed work carry a different name or version number from the original work or otherwise indicate what changes have been made”. 
  3. Share-alike: “The licence may require distributions of the work to remain under the same licence or a similar licence”. 
  4. Notice: “The licence may require retention of copyright notices and identification of the licence”.
  5. Source: “The licence may require that anyone distributing the work provide recipients with access to the preferred form for making modifications”. 
  6. Technical restriction prohibition: “The licence may require that distributions of the work remain free of any technical measures that would restrict the exercise of otherwise allowed rights”.
  7. Non-aggression: “The licence may require modifiers to grant the public additional permissions (for example, patent licences) as required for exercise of the rights allowed by the licence. The licence may also condition permissions on not aggressing against licensees with respect to exercising any allowed right (again, for example, patent litigation)”.

The most common type of open licences that systematise the above conditions are those of Creative Commons (i.e. CC licence). Each CC licence contains a different combination of conditions and permissions that a work is subject to. The following chart depicts the most commonly used types of CC licences.

The Creative Commons license types CC Attribution- Share Alike 3.0
4 All definitions are quoted verbatim.
5 All definitions are quoted verbatim.

1.1.4 Why Open data?

If copyright aims to protect originators of intellectual works, then why has the need for openness emerged? In other words, if someone particularly focuses on the case of data the question transforms into: “why should data be open?”. The answer to this question is provided through a series of questions related to the value of open data.

A. Who benefits from open data? 

If data were under the possession of one person, organisation/company or government, protected by Copyright, then there would be extreme costs for accessing and utilising them. Thus, they would be practically inaccessible to individuals, communities and organisations. Instead, many studies (i.a. Allam & Dhunny, 2019; Zhao & Zhang, 2020) have pointed out that open data have a multiplicative value for economies, since they give rise to the elaboration of processes and the acceleration of innovation (The open data handbook, n.d.).

B. What are the costs associated with data? 

It is well established that data has only a cost of production and minimal or no cost of reproduction (i.a. Benkler, 2006; Rifkin, 2014; Stigler, 1961). As raw data is not usually considered an intellectual output, it is questionable whether any Copyright protection should be applied to them. 

C. To whom does data belong? 

A critical issue related to data, is data ownership. As it will be further explained below (see CARE principles, section 1.2), data belong to those who produce them, not to those who process them, after collection has occurred. What seems to be challenging is for someone to delineate the concept of the ‘originator’, since originators are not usually those who possess the data, but the individuals who produced the data (the concept will be elaborated more in section 1.2). 

D. How can data accelerate innovation? 

In most cases, data is the final output of a process through which the produced data were collected via a (separate) monitoring process (e.g. traffic data collected through the GPS system). Openness applied in data offers individuals, organisations or governments the opportunity to combine them with other datasets that have never been combined before or utilise them in innovative ways. In other words, the more accessible data is, the more possibilities for new knowledge areas and hence innovative solutions and products are to emerge.

E. How does open data enhance multistakeholders collaboration?

By focusing on the value of multistakeholders collaboration that evolves around open data, there seems to emerge four possibilities of collaboration ​​(Meijer et al., 2019). The first is exclusively about data themselves, the combination of which could lead to new knowledge areas. The second is related to the promotion and establishment of innovative types of collaboration among different stakeholders. The third is about the social bonds developed among citizens, while the fourth involves a variety of actors such as governments, civil society organisations, public institutions etc. Hence, conceptualising what is described here, the term ‘multicentricity’, closely linked to openness, emerges and is defined as the development of networks of collaboration among multiple centres in society ​​(ibid.). 

F. How does open data contribute to active citizenship?

Data that are accessible to individuals who produced it fosters democracy, transparency and citizen participation, allowing citizens to become more aware of the meaning and value of their actions. Hence, in an open governance paradigm, where open data lies at its heart, citizens, namely individuals who are members of a community and from which certain legal rights and duties are enshrined by law (Center for the Study of Citizenship, n.d.), would become empowered to critically influence public policies and decisions through citizen engagement (Meijer et al., 2019). In this context, since open governance is defined as those innovative, bottom-up forms of action aimed at solving complex public issues, and is based on the fundamental changes derived from the widespread use of network technologies and open data, citizen deliberation6 and social action7 can become catalysts of governance (Meijer et al., 2019), as depicted in the schema below. An indicative example of an open governance initiative, combining citizen engagement with open data, is the citizens’ reaction to Canterbury’s earthquake in New Zealand in 2010. In this case, university students, in collaboration with public entities and civil society organisations, created an online regional map, using open governmental data on which people could pin locations where fresh water, power, gas or roads were blocked or damaged.

In conclusion, openness seems to be a radically new concept pushing societal change in completely new directions that could prompt progress and development in a more democratic and transparent way. Hence, open data seems to play a central role in this process.

7 The term ‘citizen deliberation’ refers to the process of citizens discussing and debating public issues. The goal of citizen deliberation is to inform citizens on issues related to public policy as well as to stimulate them to participate in the formation of it. For more information on this concept see the review of Delli Carpini et al. (2004).
8 According to Budiman (2023) a general and simple definition of the term ‘social action’ is any “action that is influenced and affects other people during social interactions”. For more information you can visit this online course.

1.2 Data governance and Indigenous communities

In section 1.1.4, we dealt with the issue of data ownership. The main argument that was put forward was that data does not belong to the organisation or individual that manages it, but to those it derives from. To acquire a better understanding of the issue, a necessary distinction should be done between data sovereignty and data governance. Specifically:

  • Data sovereignty: is the (legal) concept according to which information and data, which has been converted and stored in binary digital form, is subject to the laws of the country in which it is located (Global Indigenous Data Alliance, 2022). 
  • Data governance: refers to the ownership, collection, control, analysis, and use of data (Global Indigenous Data Alliance, 2022). 

In other words, data sovereignty indicates that there might be independent legal restrictions and obligations that data may be subject to, stemming from the national legislation of the country it is located in or generated from. Hence, data may be ascribed to legislative frameworks with which its owner, manager, originator and user have to comply with. Openness, described in the previous section, is a general framework on data governance defining and regulating issues beyond national or corporal legislations. In this context, Openness may also touch upon issues of data sovereignty. Similarly, the CARE principles (see section 1.2.1) that will be introduced in the following section, aim to provide an ethical as well as coherent framework to issues related to indigenous communities’ data governance.

1.2.1 The CARE principles

The CARE principles are a set of 4 principles standing for:

The CARE principles Logo CC BY 4.0 GIDA
  • Collective benefit
  • Authority to control 
  • Responsibility 
  • Ethics
The CARE principles constitute a framework of data governance that acknowledges to indigenous communities and nations the absolute right to govern the collection, ownership and application of their data (Global Indigenous Data Alliance, 2022). Their main purpose is to safeguard that indigenous data is used ethically towards the protection of the community where it is derived from (Australian Research Data Commons, n.d.). Hence, their use is indented to protect indigenous innovation8 and self-determination, thus serving indigenous people’s wellbeing in general (Russo Carroll et al., 2021). According to the Global Indigenous Data Alliance (2022), the exact content of the CARE terms is as it follows:
The CARE principles Summary infographic CC BY 4.0 GIDA

A. Collective benefit

“Data ecosystems shall be designed and function in ways that enable Indigenous peoples to derive benefit from the data”.

According to Global Indigenous Data Alliance (2022), for the principle to be applied, the following conditions are to be applied as well:

→ C1. For inclusive development and innovation 

“Governments and institutions must actively support the use and reuse of data by Indigenous nations and communities”.

→ C2. For improved governance and citizen engagement

“Ethical use of open data can improve transparency and decision-making by providing Indigenous nations and communities with a better understanding of their identity, territories, and resources”. 

→ C3. For equitable outcomes

“Any value created from Indigenous data should benefit Indigenous communities in an equitable manner and contribute to Indigenous aspirations for wellbeing” .

B.  Authority to ownership 

“Indigenous peoples’ rights and interests in indigenous data must be recognised and their authority to control such data be empowered”. 

According to Global Indigenous Data Alliance (2022), for the principle to be met, the following conditions are to be applied as well:

→ A1. Recognising rights and interests 

“Indigenous peoples have collective and individual rights to free, prior, and informed consent in the collection and use of such data, including the development of data policies and protocols for collection”.

→A2. Data for governance