Observational equivalence is the property of two or more underlying entities being indistinguishable on the basis of their observable implications. Thus, for example, two scientific theories are observationally equivalent if all of their empirically testable predictions are identical, in which case empirical evidence cannot be used to distinguish which is closer to being correct.
(en.wikipedia.org/wiki/Epistemic_injustice)
Testimonial injustice is unfairness related to trusting someone's word. An injustice of this kind occurs when someone is ignored, or not believed, because of their sex, sexuality, gender presentation, race, or, broadly, because of their identity.[3]
Fricker gives the example of Londoner Duwayne Brooks, who saw his friend Stephen Lawrence murdered.[4] The police officers who arrived at the scene regarded Brooks with suspicion. According to an official inquiry, "the officers failed to concentrate upon Mr Brooks and to follow up energetically the information which he gave them. Nobody suggested that he should be used in searches of the area, although he knew where the assailants had last been seen. Nobody appears properly to have tried to calm him, or to accept that what he said was true." That is, the police officers failed to view Brooks as a credible witness, presumably in part due to racial bias. This was, says Fricker, a case of testimonial injustice, which occurs when "prejudice causes a hearer to give a deflated level of credibility to a speaker’s word."
Hermeneutical injustice is injustice related to how people interpret their lives. (The word 'hermeneutical' comes from the Greek word for 'interpreter'.)
A concrete example is helpful. In the 1970s, the term "sexual harassment" was introduced to describe something that many people, especially women, had long experienced.[5] Consider a woman who experiences sexual harassment in the year 1960, before the term was introduced. She may have difficulty putting her experience into words. The difficulty that she faces is no accident. It is due (in part) to women's exclusion from full participation in the shaping of the English language. Now suppose it is 1980, after the term was introduced. The woman may now better understand what happened to her. However, she may struggle to explain this experience to someone else, because the concept of sexual harassment is not yet well known. The difficulty she faces is, again, no accident. It is due (in part) to women's exclusion from equal participation in journalism, publishing, academia, law, and the other institutions and industries that help people make sense of their lives. Miranda Fricker argues that some women's lives are less intelligible – to themselves, and/or to others – because women have historically wielded less power to shape the categories through which we all understand the world. Fricker claims that this is also true of other marginalized groups.
Other scholars since Fricker have adapted the concept of epistemic injustice and/or expanded what the term includes. These contributions have included naming and narrowing down forms of epistemic injustice, such as: epistemic oppression,[8] epistemic exploitation,[9] silencing as testimonial quieting and as testimonial smothering,[10] contributory injustice,[11] distributive epistemic injustice,[12] and epistemic trust injustice.[13]
A closely related literature on epistemologies of ignorance has also been developing, which has included the identification of overlapping concepts such as white ignorance[16][17] and willful hermeneutical ignorance.[18]
Kristie Dotson has warned that some definitions could leave out important contributions to the ongoing discussion around epistemic injustice.[11] Gaile Pohlhaus Jr. has replied that the concept should therefore be considered an open one, and many different approaches to the concept should be considered.[2]
In 2017, the Routledge Handbook of Epistemic Injustice was published, compiling chapters addressing both the theoretical work on the concept and efforts to apply that theory to practical case studies.[19] The Indian political theorist Rajeev Bhargava uses the term epistemic injustice to describe how colonized groups were wronged when colonizing powers replaced, or negatively impacted, the concepts and categories that colonized groups used to understand themselves and the world.[20]
Normal lists and tables take longer to scan through the bigger they are, right? A hash table is a trick that programmers have figured out, so that in a hash table the cost of looking up a value is independent of the amount of items in the table. For small data it's not so appropriate, but for enormous data it's the only appropriate data type. At least if you're going to be accessing values often.
How does this work?
Graham considers the hierarchy of programming languages with the example of "Blub", a hypothetically average language "right in the middle of the abstractness continuum. It is not the most powerful language, but it is more powerful than Cobol or machine language."[21] It was used by Graham to illustrate a comparison, beyond Turing completeness, of programming language power, and more specifically to illustrate the difficulty of comparing a programming language one knows to one that one does not.[22]
Graham considers a hypothetical Blub programmer. When the programmer looks down the "power continuum", he considers the lower languages to be less powerful because they miss some feature that a Blub programmer is used to. But when he looks up, he fails to realise that he is looking up: he merely sees "weird languages" with unnecessary features and assumes they are equivalent in power, but with "other hairy stuff thrown in as well". When Graham considers the point of view of a programmer using a language higher than Blub, he describes that programmer as looking down on Blub and noting its "missing" features from the point of view of the higher language.[22]
Graham describes this as the "Blub paradox" and concludes that "By induction, the only programmers in a position to see all the differences in power between the various languages are those who understand the most powerful one."[22]
Each new feature begins with writing a test, which also means you specify exactly what the feature should do. Then write code to pass the test, nothing more.
This is a methodology for meta-analysis that only got popular recently, like around 2018.
In hindsight, it's weird that we didn't have it from the beginning. How did anyone do science before? Seems like dumb luck science even got started. Guess actually it was dumb luck; science didn't start for the ~5,000 years after writing, so yeah, in alternate possible worlds we might even now be in the Post-Late-Middle Ages!
It's so important to preserve a good understanding of what makes science work, to carry us through an eventual global collapse… but I digress.
Systematic review is often described with five phases: "Search, Dedupe, Screen, Extract, Report."
Depending on your needs, this process may become cyclic, i.e. you loop back to Search and start again. That doesn't apply to writing a grad paper (since you write it once and forget it), but does apply if you are responsible for developing a line of MRI machines.
There's also rapid review, which drops some parts of the process in favour of efficency. There's even interactive rapid review (ebib:ricoGuidelinesConductingInteractive2020) which started in evidence-based software engineering (EBSE), likenable to agile development.