OLDaily

Building Theories from Case Study Research
Kathleen M. Eisenhardt, The Academy of Management Review, 2022/09/08

As noted in Quartz, this paper "would lay the foundation for decades of management research." The case study method is employed to use detailed accounts of specific companies in order to develop wider-reaching theories of management. If we view the method strictly from a statistical perspective it's quite weak: the examples are few in number and possibly not widely representative. But it does allow people to work with rich data unsuitable for statistical or even scientific analysis. To my mind, it's a mechanism for stimulation pattern recognition, and its success is based not only on the details of the case but also the investigator's background and experience; this is what helps them identify salient similarities between the cases. And as a teaching tool, to my mind, they perform a similar function, only in reverse, using detailed cases to put students into a series of rich environments where they can develop, tacitly, enough background and experience to get started in the real world.

Web: [Direct Link] [This Post]

The Merge
2022/09/08

You may be reading about something called 'the Merge' over the next couple of weeks (it's tentatively scheduled for September 19). This is the conversion of the Ethereum blockchain network from proof-of-work to proof-of-stake. Ethereum is the second-largest blockchain network in the world, after Bitcoin, and so the Merge will have huge implications for digital currencies and distributed ledgers generally. The Merge will result in a huge energy savings because "it eliminates the need for energy-intensive mining and instead secures the network using staked ETH," the Ethereum currency. This website is an introduction to the Merge but not a complete explanation; this article offers more detail. See also this article in Protocol.

Web: [Direct Link] [This Post]

Making Lifelong Learning a Reality: A Handbook
David Atchoarena, et al., UNESCO, 2022/09/08

As we read in the introduction, "this handbook (197 page PDF) provides information, evidence and basic conceptual models to facilitate the adoption of life-long learning (LLL) in national and local settings." According to the book, LLL applies to all ages, all levels of education, all learning modalities, all learning spheres and spaces, and supports a variety of purposes. The case for LLL can be made by pointing to technological, geographic or demographic changes, among others. It recommends proponents "place LLL firmly on national policy agendas, and identify public policy issues where the adoption of LLL has significant comparative advantages" (p. 16) and continues along that line, linking LLL to (for example) the sustainable development goals (SDG) and tracking this to recommendations for implementation. The handbook concludes with a case study of a 'learning city'.

Web: [Direct Link] [This Post]

Can GPT-3 be honest when it speaks nonsense?
Ben Dickson, TechTalks, 2022/09/08

To 'be honest' is to have the possibility of telling a falsehood, but to choose not to do so. It's something that can be attributed to devices - we would ask, for example, whether a scale gave an honest weight. But normally we think it has to do with intention; the agent has to know what they're saying isn't true. But if an AI simply can't distinguish between truth and falsehood (as appears to be the case when we look at some of the nonsense it produces) then there's no way to say it can be honest. Right? Well, not so fast. As this article points out, while an AI is always ready with a response to a question, it can calculate degrees of uncertainty about the answer. "Having language models express their uncertainty is a crucial aspect of honesty: there will always be things that models don't know for sure, and so uncertainty is necessary for conveying the model's knowledge faithfully."

Web: [Direct Link] [This Post]