When deliberating in person, merit refers to a critical look at the influences on our thinking and motivations. People judge recommended actions by virtue of their feasibility with respect to meeting real needs. We tend to scrutinize conclusions by the strength of logical thinking about valid facts, and feelings by how well they align with our own emotions related to the circumstances. A thorough deliberations considers the full gamut of factors.
For example, in a medical emergency with no one else around, the action to call for help right away is merited by the urgent and dire facts, the logic of seeking expert help, and the human need for health – usually accompanied by human emotion to compel action.
Online ranking systems don’t reflect the nuanced response to information. Public ushin handles merit as a crowd-sourced, customizable system of ranking and filtration.
Users can set general preferences and adjust filters while creating and browsing.
The roles of curator, vetter, and host are distinct
– Everyone should be able to curate (set filters for) content. If you create a filter and share it with others, they should be free to use, distribute, modify, and distribute a modified version of your filter (see https://www.gnu.org/philosophy/free-sw.html ). It’s likely that many users will simply adopt the filters created by someone they trust, perhaps a popular figure. There may be a default filter, managed by us or by whatever community maintains this system.
– Everyone should be able to vet another person by using a private (GPG?) key to sign that person’s identity a revocable signing key, which indicates to a reader that the author has been vetted by the organization. This also serves as a form of authentication. By using keys to vet other people, any individual or group can vet someone else.
– Everyone should be able to store and share their data how they see fit. Currently, I am interested in exploring IPLD ( https://ipld.io/#features ) to structure ushin data. You can read about Layer 0, 1, and 2 for a brief overview ( https://github.com/ipld/specs ).
Crowd filtering removes garbage
Ranking is nuanced
It may be possible for a user to weigh someone’s merit valuations of someone else, perhaps by adopting their filters, but in general the app derives merits through calculations including collective valuations of each of the regions and their contents.
I’ve translated the italicized guess below to a vision that relies on valuing the different regions, and/or the current content in the regions, independently.
“Let’s say that I choose to give weight to Alice’s merit valuations, and Alice gives weight to Bob’s merit valuation, and Bob gives weight to Charlie’s merit valuations. If Alice, Bob, and Charlie all give the same semscreen different rankings a user could calculate merit based on some function of the weight they each give.“
When user includes Alice’s input, and ranks her among top sources of information to appear in general, e.g. she has won user’s trust for her as having contributed input which has met other people’s needs – needs that the user prioritizes.
Users apply merit to points and semscreens
Given the screen shows a copy of another person’s, or own earlier message, in a new currently editing semscreen. Uses can apply merit, or demerit, to any point when the semscreen is open, or per semscreen when the semscreen is closed.
One idea had been for users to show how central a point is to their message by dragging it closer to the focus.
Points gain merit when the current user, and her trusted others, further substantiate the weight of a semscreen, and/or individual points, with evidence, logic, ethos, pathos and/or rationale for why the point matters.
Points and semscreens appear when user and user’s filters don’t exclude the kinds of people and other shapes, among those with input matching search criteria.
People and groups will be able to rank messages based on the value of each point, by DnD or up/down arrows or typing in values.
Users can re-set individual rankings and can visualize different combinations, on blank open space, by changing combinations of shapes, key words, time intervals and other filters.
Part of the determination of merit comes from a calculation based on the inherent parameters of value for each shape. Each shape has its own ranking system, and individual points and semscreens are ranked based on how well they meet those standards as well as keyword and time parameters.
- Facts by experience, evidence, citations, time, places and objects, recency
- People by credibility, degree of separation/conflict of interest, expertise, reputation, responsivity to queries, and input success in meeting others’ needs.
Each user’s findings that show in the display first – at the top of the list, in view are based on user’s ranking of kinds of people considered experts, or are otherwise trustworthy to the user as sources of information, ranked by qualifications specific to the point.
- Thoughts by cogency, strength of argument, kind of structure, rigor of analylitic method, earning demerits for logical fallacies
- Feelings by intensity and character
- Needs by basic survival
- Topics by affect of influence and relevance
- Actions by feasibility, efficiency and weighted factors influencing the likely success of meeting stated needs
If, for example, a user has highly ranked professional journals as the only citation acceptable, information from only those kinds of journals will show.
Complex deliberations in a mature ushin system will reveal the accounting methods used and standards of measurement in producing a numerical value for merit. The resultant numbers determine the prominence of information.
Later methods may apply merit to connections between and among points.
For more complex situations, deliberation suggests that not only do we consider an issue from all angles, but that we can take a second pass over our supporting points to weigh each according to their shape-based valuations.
Individuals and groups use the app tools to deliberate and assign weights and measures for different kinds of input to customize their own consensus building tools.
In U4U merit will be introduced as a cumulative value of the weights of each supporting point. Weights and measures will be customized by each user and their groups, or pursuances, to quote pursuance.
An action may be deemed to have greater merit as more profoundly needy people are more profoundly helped. Users, groups, and pursuances create autonomous ranking systems, determining merit of input transparently
The notetaking ushin app, u4few, is not used for deliberation and there is no input field for merit. When speakers in a meeting declare that something has merit, the phrase would be included in the thoughts region.