The ‘category vectors’ behind AI project The Magic See Through Mirror and all our content and projects

Taxonomy, Ontology & Hierarchy

We all need a bit of order in our lives, and See Through News is no exception. In the digital age, when it’s becoming more important to make sure robots and algorithms ‘understand’ the same things in the same way as humans do, a disciplined, consistent, logical labelling system is taking on a whole new importance.

‘Ontological taxonomy’ is computer science jargon for this field of study. Its modern flavours, like OWL are so removed from public realm they can get away with ironically illogical acronyms (OWL, somehow, stands for Web Ontology Language, but clearly sounds better than WOL).

This article explains the ontological taxonomy that underpins all See Through News content. It’s not always obvious and visible, because we wouldn’t want our nerdery to get in the way of a good story, but it is omnipresent.

There are an infinite number of different ways of categorising the world, but our taxonomy combines some commonly used international standards to suit our Goal of Speeding Up Carbon Drawdown by Helping the Inactive Become Active.

Like everything See Through News does, it’s open source. We publish it here to help explain the logical structure behind STN’s range of apparently disconnected projects, and to demonstrate how all these divergent starting points lead to the same destination.

If you’d like to use it for your own activism content, help yourself. If you’d like to understand more about why it’s so important, and how to use it to categorise and tag your own content, do contact us and we’d be happy to explain further.

The more widely this taxonomy is adopted, the easier it will be for the algorithms that increasingly dictate what’s put in front of us, to show us the good stuff…

AI for Good

See Through News is far from the only non-profit seeking to turn to good use AI, the technology that defines and drives the modern world.

Our work is based on the work of many others also working in an open-source, Creative Commons spirit to use tech to solve humanity’s most pressing problem, rather than sell us more stuff we don’t need and can’t afford.

This is a work-in-progress description of the taxonomy See Through News is developing not just for its AI projects, but also to tag, order and structure everything we do.

This taxonomy is one of the 5 Vectors (others listed below) we propose to use to tag the training data for the STUNTS database that will form the basis for the Magic See Through Mirror interface.

This interface is a highly ambitious, but technically feasible project, designed to promote STN’s Goal of Speeding Up Carbon Drawdown by Helping the Inactive Become Active.

Here’s a real-world example of how it would work. 

Here’s a more technical description of the cutting-edge AI involved.

We welcome any comments, edits or suggestions.

The same underlying taxonomy is integrated into all See Through Carbon projects, from our website to our carbon auditing standard See Through Carbon, to our YouTube channel. Consistent and logical categorisation helps any human-based organisation, but is essential for any computer science application.

Vector #1: Content Taxonomy 

1. Protecting
   1. Air
   2. Biodiversity     
   3. Soil
   4. Trees
   5. Water  i) inland ii) ocean

2. Stressing
   1. Climate
   2. Disasters  i) natural   ii) manmade
   3. Hydro
   4. Population
   5. Waste  i) reduction      ii) disposal
      
3. Sustainability
   1. Building i) fixing      ii) construction
   2. Energy   i) reduction  ii) renewal
   3. Food
   4. Plastic i) reduction ii) disposal
   5. Transport  i) land   ii)  sea iii) air

4. Influencing
   1. Culture i) Arts ii) popular
   2. Law i) enforcement ii) legislation
   3. Media i) Old ii) New
   4. Money i) private ii) public
   5. People i) us ii) them
   6. Politics i) voting ii) pressure
   7. Science 

Vector #2: Content Complexity: see Nugget Writer & Tagger Guide

1. Beginner
2. Student
3. Teacher
4. Expert 

Vector #3: Content Scope 

1. Local
2. County
3. Regional
4. National
5. Global
   

Vector #4: Content Source (example) 

Local 

1. Citizen/Community (you as Salisbury resident)
2. Media (Salisbury Journal)
3. Government (Salisbury City Council)
4. Business (Salisbury Chamber of Commerce)

State/County

1. Citizen/Community (you as Wiltshire resident)
2. Media (Wiltshire Times)
3. Government (Wiltshire County Council)
4. Business (Wiltshire Chamber of Commerce)

Regional

1. Citizen/Community (you as Wiltshire )
2. Media (Western Gazette)
3. Government (n/a)
4. Business (Business West)

National

1. Citizen/Community you as UK citizen)
   1. Media (The Times)
   2. Government (Westminster/Holyrood/Welsh Assembly)
   3. Business (Boots)

International 

1. Citizen/Community (you as human being)
2. Media (Time Magazine)
3. Government (UN)
4. Business (Coca-Cola)

Vector #5: Content Impact : Good or Bad News?

This needs more work, but our provisional model is:

1. Significantly negative
2. Negative
3. Minimally negative
4. Neither positive nor negative
5. Minimally positive
6. Positive
7. Significantly positive

Post-Hoc Gold Standard Metrics for Transition to Activist

These are still in development, and can only be tested once a sufficiently large tagged database of content has been created. Good planning is never time wasted, though, and it’s helpful to think ahead to how we can test and evaluate our tests and evaluations.

Vector #6:  Emotional impact on reader: (retrospective spot checks via surveys/focus groups, exact methodology TBC)

1. No impact
2. Minimal impact
3. Some impact
4. Significant impact
5. Powerful impact

Vector # 7 Behavioural impact on reader: (retrospective spot checks via survey/focus group)

1. No influence on my thinking
2. Made me think differently
3. Made me try to change views of friends and family
4. Made me try to change views of work colleagues
5. Made me try to change official policy

Metrics for achieving Speed Up Carbon Drawdown Goal 

This is the key metric – are Inactivitists really becoming Activists?  If so, how many, how much, and in what ways are their Actions speeding up carbon drawdown?

But it is also the most elusive to measure.

Consistent questionnaires over time will track relative progress, but absolute evidence of Inaction becoming Action is very hard to implement, principally because:

a) many outcomes will be self-reported and impractical to verify

b) by their nature they’re post-hoc, requiring retrospective monitoring rather than real-time tracking

c) which is expensive

Some potential indicative/proxy metrics:

1. Voting for parties with policies prioritising carbon reduction.
2. Emailing local MPs on specific carbon-reduction policies.
3. Changes in personal behaviour (plant-based diet, getting rid of car, rooftop solar PV cells  etc.).
4. Signing petitions for carbon reduction.
5. Campaigning on carbon-sensitive votes.

Suggestions and comments welcome!