The category vectors behind The Magic See Through Mirror and STUNTS

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 for its AI projects.

This will be used as one of the Vectors (other draft Vectors are listed below too) for the STUNTS database.

This database will form the basis for the Magic See Through Mirror interface.

This interface is 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.

Vector #1: Content Taxonomy 

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

2. Stressing
   1. Hydro
   2. Population
   3. Waste  i) reduction      ii) disposal
   4. Disasters            i) natural   ii) manmade
   5. Climate
      
3. Sustainability
   1. Food
   2. Cities  i) fixing      ii) building
   3. Transport      i) land   ii)  sea       iii) air
   4. Energy   i) reduce       ii) renew
   5. Plastic i) reduce ii) dispose

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

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 Acitivist

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 [NOTE: CREATE DRAFT QUESTIONNAIRE] 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 please…