Merge pull request #531 from lauerj/main

Pull request in response to issue #221

contents/english/06-03-media.md

@@ -47,7 +47,7 @@ While many Americans look back with nostalgia on the history of the press, the e
 
 The "Deliberation" chapter above suggests a natural strategy.  Social media algorithms could "communities" based both on patterns of behavior internal to the platform (e.g. views, likes, responses, propagation, choices to join) and on external data such as social science or group explicit self-identification (more on this below).  For each such community, the algorithms could highlight "common content" (commonly agreed facts and values) of the group that span the divides internally, as well as important points of division within the community.  Content could then be highlighted to citizens of the communities within this social context, making clear which content is rough consensus in which communities that citizen is a member  and which content is divisive, as well as offering opportunities for the citizen to explore content that is consensus on the other side of each divide from the one she is on within that community. 
 
-Such a design would continue to offer individuals and communities the agency social media affords them to respectively shape their own intersectional identities and self-govern.  Yet at the same time it would avoid the rampant "false consensus" effect where netizens come to believe that extreme or idiosyncratic views are widely shared, fueling demonization of those who do not share them and a feeling of resentment when associated political outcomes are not achieved or "⿻istic ignorance" where netizens are unable to act collectively on "silent majority" views.  Furthermore, and perhaps most importantly, it would reshape the incentives of journalist and other creators away from divisive content and towards stories that bring us together.  Furthermore, it is relevant beyond "hard journalism" *per se* as many other cultural forms (e.g. music) benefit from audiences who want to share cultural objects and fandom with other.
+Such a design would continue to offer individuals and communities the agency social media affords them to respectively shape their own intersectional identities and self-govern.  Yet at the same time it would avoid the rampant "false consensus" effect where netizens come to believe that extreme or idiosyncratic views are widely shared, fueling demonization of those who do not share them and a feeling of resentment when associated political outcomes are not achieved or "⿻istic ignorance" where netizens are unable to act collectively on "silent majority" views.[^Note] Furthermore, and perhaps most importantly, it would reshape the incentives of journalists and other creators away from divisive content and towards stories that bring us together.  It is relevant beyond "hard journalism" *per se* as many other cultural forms (e.g. music) benefit from audiences who want to share cultural objects and fandom with others.
 
 
 ### ⿻ public media
@@ -65,6 +65,7 @@ This might play out in a variety of ways, but a simple one would be for particip
 
 Overall, the examples above show how ⿻ can empower a new pro-social, ⿻ media environment: one where we can connect deeply with others from very different from us, where people come together to tell their stories in authoritative and verifiable ways without compromising community or individual privacy and where we come to understand what unites and divides us in the interests of the dynamism and solidarity of all our communities.
 
+[^Note]: An example of false consensus is that many observers believe SARS-Cov-2 escaped from a laboratory ('lab leak' hypothesis).  The rationalist web site Rootclaim (https://www.rootclaim.com/) even assessed 'lab leak' at 89% probability (~8 to 1 in favour).  Subsequently, educated laypersons were exposed to the evidence (e.g. Pekar et al., Science 377, 960–966, 2022 and Worobey et al., Science 377, 951–959, 2022.) in over 18 hours of adversarial debate and found posterior probabilities on the order of ~800 to 1 *against* lab leak, implying a Bayes factor of ~100,000 to 1 against lab leak. Despite the strength of the evidence, the lab leak claim persists since not only does zoonosis lack emotional resonance but it also requires hard work to evaluate and offers no cathartic pay-off.  Similarly, due to ⿻istic ignorance, despite the fact that more than 81 million people in the United States voted for Joe Biden in 2020, a small crowd of several thousand highly motivated individuals almost succeeded in disrupting the Electoral College vote count on 6 January 2021.  
 [^Publicmedia]: https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2017-11/Public%20support%20for%20Media.pdf
 [^Religiousmedia]: https://www.causeiq.com/directory/grants/grants-for-religious-media-organizations/
 [^Twitterrev]: https://www.statista.com/statistics/271337/twitters-advertising-revenue-worldwide/