“What features should we look for when we are selecting a recognition technology? Are there specific warnings or highlights that should be taken into consideration?”
These issues have been addressed during MIRVA workshop at ePic conference in October 2019, as sharing progress development and gathering expert feedback on Recognition Guidelines for Technology providers.
The workshop introduction posed two main pillars for participants discussion: user stories and a proposed set of technology features.
On one hand user experience and needs must always be at the center of technology research and design, so the reflection started with personas inspired by the project Future of learning Preparing for change and tried to picture how – and which – recognition technologies could help them in their personal path: two personas have been chosen and a backstory for them has been outlined in order to propose a sufficiently varied set of cases, covering both young and mature users, well-known and emerging technologies, and a reasonable array of social challenges posed by the context in which user personas are located, in order to show the relevance of recognition technologies in and beyond socially striving contexts.
On the other hand one output of Recognition Technologies research has been proposed: a set of dimensions meant to support and guide analytical evaluation of said technologies. The purpose was to see how this framework would apply to the technologies considered by the public, which outcomes would arise from, if some dimensions could effectively be used to identify technologies for open recognition, if such a distinction would be helpful to users and to test the framework itself verifying if new dimensions should be added or some could be collapsed.
The stories of Chloe and Raymond lead participants to include as recognition technologies also widely diffused technologies and technological platforms – mainly social networks such as Youtube, Instagram, etc – that do not have recognition among their main purposes but happen to be used and to be perceived as very relevant in that sense by the vast majority of users.
Indeed, what is a “like” if not a form of recognition? And isn’t the importance every one of us places in receiving social network based confirmations the first proof of how fundamental recognition is for one’s own identity building? How can this widely understood relevance combined with the closure of most such platforms not be a threat to people’s identity, valorization and well-being?
In fact, the much mediatically exposed and slightly mocked tragedy of influencers losing all their wealth of follows and likes due to a one-directional platform decision, or a sudden change in platform trends, could be understood as examples of the risk posed by placing one’s own recognition wealth inside a closed non-portable network, which is of course the case with proprietary social platforms, but also with circumscribed territorial networks.
The outlined context and proposed reflections fostered a relevant and constructive discussion on
- the role of communities in recognition and the role of technology for communities
- the role of recognition in identity building and narrative over time and in relation to communities
- a parallel between the need for recognition and the phenomenon of fake likes
- the relevance of recognising “unintended” learning
- the need to improve the general relevance attributed to trustable informal recognition
- the need to connect informal recognition to formal as one way to improve trustability
- portability as a relevant dimension that marks the specific usefulness of open recognition technologies as opposed to any other general recognition technologies.
Communities, technologies, recognition, agency
The relationship of an individual with the communities in which his/her recognition happens is at the same time tight and complex. Most recognition cases happen within a community, which makes the recognition value strongly connected to the recognition of the community itself: the recognition of the relation between individual and community is therefore reciprocal and mutually reinforcing, but external recognition is fundamental to connect with other communities, other contexts and other individuals.
On top of this, each individual belongs to different partially overlapping communities at the same time and his/her relation to communities is subject to change over time, just as communities themselves are subject to change, loose or grow reputation, even disappear.
In this perspective technologies – even social technologies – are better understood as windows on communities than as the founding ground of communities: communities exist beyond technologies even when their are largely based on them (e.g. online fora communities) and their understanding should not be collapsed to the understanding of the currently underlying technologies.
All this considered, the agency about one’s own recognition should be placed in the hand of the person, who needs to be able to use recognition and recognition networks to build his/her own narrative of his/her own identity – which again can change over time. Being recognized as something is a powerful identity builder and as such can empower or cripple.
Recognition relevance and trustability
When considering widely used social networks as recognition platforms, the relevance attributed by all users to recognition appears largely undisputable: the need for recognition is in fact what drives most actions on social platforms. The value of trustable recognition, on the other hand, seem to be less understood and highlighted by the general user and by the usual activities on platforms. Only when a relevant decision is at stake (e.g. evaluating a possible collaborator over Linkedin, or an influencer to engage) the need emerges to verify how trustable the recognition wealth of a person really is. While a lot can and should be done on an educational perspective, from a technological point of view there seems to be an empty space in exposing recognition value gathered from a recognition network, i.e. evaluating the relevance of single and combined recognition pieces based on the value of source, content and further network links, with a mechanism similar to that of link rating for search engines. Of course such a mechanism would also place many threats and should be carefully designed and presented.
Recognition portability
While analyzing both user stories it appeared clearly that the need for open recognition technologies might not arise for the individual user unless the issue of portability becomes crucial. A worker like Raymond, whose recognition wealth is placed in a strict territorial word-of-mouth network, probably won’t encounter any problem at all unless he/she needs to radically change his/her context, risking to lose his/her recognition wealth and so needing to find a way to export it to the next context. Similarly an online social-network based recognition (and customers / followers) network such as that built by Chloe will work just fine until something happens that forces the user to find a way to migrate to a different system (or set of systems) with an analogous risk of losing a considerable portion of his/her recognition wealth.
When evaluating a recognition technology to adopt – both as an individual and as a community or project leader – is therefore important to consider how portable it is and evaluate the risks of lock-in and wealth loss compared to the simplicity of choosing widely diffused and probably more user friendly and (apparently) cost-effective existing platforms. Portability is usually a consequence of the openness and the level of standard use of a technology, but is the actually relevant concept to be addressed in this context.
Implicit learning
A last relevant point emerged from the discussion with workshop participants is the fact that, just like a social platform is not considered a recognition platform but works as one, also many platforms are not learning platforms but their use at a given level implies a degree of learning – often a very relevant one – which is often implicitly evaluated by human readers (e.g. when evaluating to hire a blogger in a media marketing position) and would probably deserve to be made more explicit. In this sense the evidence mechanism could probably be an answer.
Interested in participating?
You can participate in the MIRVA project by filling in the survey to collect and analyze open recognition technologies. Handout material from the workshop is also available.
Get in touch with us for questions or other related subjects.
