Seriously, this is the last time I’m posting anything about statues. The first is a thoughtful piece that compares how we think about morality in history to the progress of science. The second is a podcast that delves into the questions around why we care about monuments and statues. For previous posts about this topic, click here.
Are these long-overdue corrections in the name of social justice, or simply ideologically driven acts of anti-historical vandalism? The answer depends on how we judge the moral actions of figures from the past, a question that in turn requires us to consider the nature of morality itself…
Knowledge is a relay race. It is a fundamental misunderstanding about how it works to criticize a swift runner who effectively passed the baton because he did not complete the race on his own….
All of which to say, there is a vital difference between being wrong and being blameworthy….
When it comes to praise and blame, intention and context matter, not just a snapshot of the final result.
The Inquiry Podcast: Why do we care about statues?
The killing of African American George Floyd ignited anti-racist protests around the world – many centred on statues associated with colonialism and slavery. Why do these figures of bronze and stone generate such strong feelings? And what do they tell us about how countries deal with their past?
How can we grasp nature’s image and put it on a page? How do we judge the truthiness of images of nature? These questions are particularly challenging when it comes to images of the far reaches of galactic and oceanic space, which share a quality that we might call sensory distance…Visualising places of sensory distance requires distinctive approaches, not merely to collect information but also to interpret it, since the information gathered is patchy. In the absence of comprehensive and accessible information, acquiring knowledge about sea monsters and black holes calls for imaginative image-making…
Although today’s black hole image emerges out of different material technologies, it, too, relies on techniques of imaginative prototyping of distant objects – about which only limited evidence can be gathered, by circuitous means – and collaborative practices of visualising…
Natural objects exist in the world independently of our knowledge of them and – what is key here – independently of any particular community’s knowledge of them…
The imaginative extrapolation involved in prediction and expectation is also crucial to the discovery of black holes: the laws of gravity suggested that this species of space entity existed, in theory, long before the first black hole was discovered in 1971. Just as naturalists had to imagine the animal whose head had once sported a tusk…
The metaphors of neuroscience – computers, coding, wiring diagrams and so on – are inevitably partial. That is the nature of metaphors, which have been intensely studied by philosophers of science and by scientists, as they seem to be so central to the way scientists think. But metaphors are also rich and allow insight and discovery. There will come a point when the understanding they allow will be outweighed by the limits they impose, but in the case of computational and representational metaphors of the brain, there is no agreement that such a moment has arrived. From a historical point of view, the very fact that this debate is taking place suggests that we may indeed be approaching the end of the computational metaphor. What is not clear, however, is what would replace it.
How a drug became an object lesson in political tribalism.
Why are we seeing the polarization over hydroxycholorquine, then, in spite of the serious consequences? The explanation may lie in the kind of information available to the public about COVID-19, which differs importantly from what we see in other cases of polarization about science. When it comes to the health effects of injecting disinfectants, there is no uncertainty about the massive risks. And for that reason, we don’t expect polarization to emerge, even if Trump suggests trying it. But even the best information about COVID-19 is in a state of constant flux. Scientists are publishing new articles every day, while old articles and claims are retracted or refuted. Norms of scientific publication, which usually dictate slower timeframes and more thorough peer review, have been relaxed by scientific communities desperately seeking solutions. And with readers clamoring for the latest virus news, journalists are on the hunt for new articles they can report on, sometimes pushing claims into prime time before they’ve been properly vetted.
Historians believe that the past is irreducibly complex and the future wildly unpredictable. Scientists disagree. Who’s right?
‘Historical facts’ are not discrete items, awaiting scholars to hunt them down. They need to be created…
The danger here, of course, is that these approaches tend to assume that the natural sciences are capable of producing objective knowledge, and that mirroring their methodologies will produce ‘better’ knowledge for the rest of the academy. Half a century of research in the history of science has shown that this perspective is deeply flawed. The sciences have their own history – as indeed does the notion of objectivity – and that history is deeply entwined with power, politics and, importantly, the naturalisation of social inequality by reference to biological inferiority. No programme for understanding human behaviour through the mathematical modelling of evolutionary theory can afford to ignore this point.
Artist Zach Weinersmith teams up with FiveThirtyEight to break down and explain the challenges in building accurate models for the pandemic from differing assumptions, data collection, and just a general lack of information.
Interesting quote from the cartoon: “Every variable is dependent on a number of possible choices and gaps in knowledge.”
Great article for discussing the production of scientific knowledge, shared knowledge, and also the new knowledge and technology theme.
Scientific Cooperation Knows No Boundaries—Fortunately
Infectious diseases, it is commonly said, know no borders, and neither does the knowledge needed to fight them. Scientists around the world routinely share information and collaborate across borders. The current pandemic has scientists working together on platforms such as Slack, and using new tools, such as machine learning, to rapidly detect the novel coronavirus in tests that use large amounts data from multiple sources. This outbreak has demonstrated in real time how scientific understanding can indeed be a global public good.