It is, probably more than is apparent. See:
https://en.wikipedia.org/wiki/Philosophy_of_science
https://en.wikipedia.org/wiki/Sociology_of_scientific_knowle...
There is an interesting recursive problem here, though: what tools do you use to scientifically analyze the scientific process? Whatever tools you use will themselves be hobbled by the same systemic flaws you are trying to understand.
Also, as any sociologist will be happy to tell you, incentive structures and other human group behavior gets in the way. It's probably hard to get funding for a study that shows that all the other departments at your university aren't quite the flawless seekers of truth they appear to be.
Iterating on existing systems to see if you can get results to converge and also testing new systems to see if they also result in known good values.
All of these ideas that you tacitly take for granted are itself mutable parts of the scientific process:
* That iterative improvement and hill-climbing is an effective process for improving results.
* That replication of experiments and convergence is a truth-generating enterprise.
* That truth can be expressed numerically.
* That there are some values that are "known good". By what process? According to whom?
To be clear, I don't disagree with those. However, these rules aren't baked into the firmament of the universe. They are processes we humans have chosen to apply in our social process of reaching conensus on truth. In other words, this list here isn't physics, it's technology.
It's entirely possible to imagine a culture whose truth finding bodies don't take for granted one or more of these rules at all. That culture might be more or less effective (again, according to what metrics?), but it would still be well-defined.
* That truth can be expressed numerically.
Isn't the basic point of quantum physics that this isn't true? We can only make guesses with probabilities, but we can't know the actual truth, and therefore can't express it numerically.
Imagine you were studying ice cream flavors. You might design a study like, "We'll ask a lot of people and the flavor that the most people prefer is the best." In other words, the metaprocess you use to design your experiment itself tacitly assumes you need a numeric result. The presumption of comparison and quantifying frames the questions you even think to ask.
But you can imagine an alternate culture that when studying ice cream flavors doesn't even ask questions with numeric answers. It could be, "We'll ask a lot of people to try flavors and write poems about the experience."
We wouldn't even call this "science". Because there is a hidden border around even the term that affects how we are able to evolve the scientific process.
And no, not poems. Elements of finite groups are not numbers, but they crop up in physics frequently. Topologies are not numbers, but they're also significant in physics.
Isn't this the idea of "free will". That you get to choose for yourself the metrics you want to optimize your life for?
Now I think that you'll use a combination of learned and inherited desires for it. But the idea here is that each individual can express those desires, and then the "success" of a society is thus to maximize each individuals success, even when they differ in their metrics.
That's a made up concept as well, but I think it still stems from individual desires. We've just mostly all individually observed that an organized society that compromises with each other to maximize each and everyone's individual desires has less risk to our own desires being squandered.
The alternative would be to try to achieve power over others to maximize your desires, and maybe from history and life experience, people have found that to be not sustainable or only achievable for a few, thus your chances at it are lower.
In essence, I think I'm saying that it seems over time people know their desires, but don't know how to beat fulfill them, and this is the metric.
I don't think this is what is held by those-who-do-science-and-philosophy-at-large (though it may be a generally accepted hand-wave, I don't know). See, for example Category Theory for a branch of what-I-believe-would-generally-be-called-science that doesn't use numbers, but instead expresses things with sets and relations.
The logician is the intersection of the set of all scientists and the set of all philosophers.
https://plato.stanford.edu/entries/philosophy-mathematics/#M...
Not essential to science; in fact, there’d a major viewpoint within metascience that explicitly rejects this as popular mythology of how science works in practice, holding that models change by revolution more than evolution.
> That replication of experiments and convergence is a truth-generating enterprise.
Not part of science, in the same way that your use of “truth” later is not.
> That truth can be expressed numerically
No. While scientism may make essential connections between science and truth, science itself only depends on useful predictive models being expressable, not about truth being expressable, numerically or otherwise, or even being a coherent, meaningful concept.
> That there are some values that are "known good"
That's not only not essential to the scientific process, but contrary to something that is: that all results are contingent.
* The study of knowledge (epistemology): https://en.wikipedia.org/wiki/Epistemology
* The study of existence and what exists (ontology): https://en.wikipedia.org/wiki/Ontology
* The study of the purpose of things (teleology): https://en.wikipedia.org/wiki/Teleology
It turns out that these are hard areas of study and it requires a lot of properly focused leisure time to understand properly. Most people don't consider these things, wing it, and wind up working with a half-baked meta-meta science of their own creation ... oh, I see what you mean :-D
Engineering. If you can build something that works based on the rules theorized by scientists, they are on to something. e.g. building a skyscraper proves we know the properties of steel to a pretty good margin of error.
Every scientist should be able to answer the particular tenets that they take on faith.
Scientists do take on faith that the universe is causal and that the rules today are the same as the rules yesterday.
Yes, scientists double check these assumptions over and over, but they can never "prove" them.
This kind of introspection is important for science to set itself apart from religion, for example.
1. There is no such thing as objectivity in inquiry. There is always a scientist interpreting things, and they are always looking at things through the lens of their own biases and cultural norms. The best you can hope to do is to be aware of your limitations.
1. Statistical evidence on its own is not always a good basis for believing something to be true. Typically you also want a good theoretical model, and an understanding of the mechanism that underlies the observed phenomena (physics is good at this, psychology not so much).
3. That there is more knowledge than is detectable through statistical methods (currently at least). And that lack of evidence from statistical studies does not necessarily constitute good evidence that a theory is false.
2. This is studied in statistics classes. It is impossible to analyze data without a statistical model, so of course your conclusions rely on it. So this is also quite trivial.
3. Again, are scientists oblivious to this simple notion? I doubt it.
My understanding is that a lot of problems in science are incentive based, and that would look very similar to someone just not knowing things from the outside.
>My understanding is that a lot of problems in science are incentive based, and that would look very similar to someone just not knowing things from the outside.
I can agree that a lot of it may be probably incentive based vs 'scientists' just not being smart enough. But is is hard to glean what the proportions are. I still suspect ( based on a general assessment of people both known and on the internet) that it is the latter.
That’s a question that Richard Feynman supposedly asked his grad students. Once you answer it, I think you’ll realize that #1 is anything but trivial.
Answer below, stop reading this comment if you want to figure it out on your own.
It’s because you’re comparing the mirror image to what you’d look like if you walked around the mirror, instead of to what you’d look like if you floated over the top. This assumption of horizontal travel is incredibly deeply engrained in humans, to the point that the English language doesn’t even have up/down equivalents to the words “left” and “right”, i.e. a word that means the direction closer to your head than to your feet regardless of your orientation.
I guess technically correct, because they're mostly latin loan words.
But reading the article, are those not applicable only to body parts? Would an astronaut ever say to another astronaut, while floating in different orientations, “Hit the button to your superior”?
Either way, I think it’s at least correct to say that there are no layman words for it.
The realizations that people expect yaw rotation because that's what they are used to, or that normal day language isn't always precise (my left? your left?), seem extremely trivial to me and doesn't require any deep philosophical "insights".
In the hard sciences, you often don't even need the handwavey "swap a and b" explanation when it is much more useful to just model its behavior (ingress ray/plane intersection, normalize ingress ray, subtract twice normal vector of plane from ray to get egress ray direction).
I'm sure Feynman was a great physicist and teacher, but he's also great at just wowing people by using lots of words without saying much. Like in his famous why-is-ice-slippery video where he goes onto a completely unnecessary discourse on the nature of questions instead of just answering the dang question.
I always felt that is the perfect way to showcase the difference between education and edutainment, and I assume his lectures were a bit more substantial.
Also, just for fun: what makes calling it swapped front to back more valid than left to right? One could recover the (translated, rotated) original image through any of a front to back swap, left to right swap, or top to bottom swap. They’re all lenses, and equally valid.
Sociology in particular should always be approached highly critically, because applying those theories and reasoning in its terms often means mass control over people's free will.
Beyond that those involved in sociology seem to believe that a study is the same thing as an experiment and like to believe that constitutes proof.
Ultimately we can't really run AB experiments on society at large because we are living in; however humanity has at its disposal all of history as a case study. My point is if you really want to understand how societies interact and form, and react, and live ask a historian, not a sociologist.
I also would apply most of these comments to economics except there seems to be more diversity of viewpoints, and studies are used less than math to try and provide a veneer of respectability.
EDIT:
If someone feels that history is inferior to sociology for understanding how societies act and behave please tell me why. I want to understand where I am wrong. But I see a lot of our arguments that we are having in society nowadays the same as one's had a thousand years ago, the discussions over Social Media are basically the exact same ones people had over the printing press in Europe, I recently read "The Republic" and there were the exact same arguments I see repeated here.
So if you feel contrary please tell me why, I admit I could be wrong, but want to understand where my reasoning is flawed.
C. Wright Mills The Sociological Imagination is great (should have been taught in college to you). Thorstein Veblen's Theory of the Leisure Class is good as well. These really seemed to me like attempts to approach truth, and perhaps that's because of the time they were written in vs the time we live in now.
I'm an economist. If I threw away the half of the data that didn't support my findings, and got caught, I'd lose my job and never publish again. I'm pretty sure the same is true in other social sciences, such as psychology. This is true irrespective of the well-documented problems that the article describes, which certainly also apply in economics and elsewhere, to varying degrees.
By contrast, when historians are caught cutting sentences in half to prove their point, they don't lose their jobs. They don't even lose their Pulitzers: https://davidhughjones.blogspot.com/2020/07/can-we-trust-his...
We truly are as T.S Elot said the hollow men.
The damages when they are wrong are orders of magnitude bigger.
They are assumed to be right, sometimes even without proof, until they are tragically proven wrong.
And nobody lose their job anyway.
Have you ever seen a sociologist lose the job because proposed something to a politician that resulted in lots of people having their life ruined?
I never did, honestly.
Have the last three more recent economic and social crisis been caused by historians mistakes?
https://familyinequality.wordpress.com/2017/10/02/sociologys...
To your last point, plagiarism, for example, definitely counts as malpractice and humanities professors lose their jobs for it.
I don't think building "universal models" or observing recurring patterns through analysis of 'experiments over wider demographics and in different points in time' require the ambition to predict a single individual behavior or actions as a corollary.
The problem lies - like you said - with the policymaker. And well more generally with people who extrapolate the results of a paper inadequately.
Like, for example, I just made two universal statements, didn’t I?
It all just feels so 'loose' compared to the physical sciences.
"As such, he was a key proponent of methodological anti-positivism, arguing for the study of social action through interpretive (rather than empiricist) methods, based on understanding the purpose and meanings that individuals attach to their own actions."
https://en.wikipedia.org/wiki/Max_Weber
edit: This is then further developed by the so called Frankfurt School as Critical Theory.
imagine those proponents of 'thousands of years old tradition of philosophy' would try to accomplish anything with it. how funny that would be..
The example discussed in OP seems to fall in the category of low rigor/high popularity. I am not 100% on my history of psych research, but it seems to me that the stereotype threat was all the rage in the late 90s following the publication of Steele and Aronson (1995). OP study seems to follow a similar experimental setup as S&A with a new group of people (Asian-American women).
As far as meta-science is concerned, I think that it remains mostly a part of philosophy (as in epistemology) and the focus of a few (senior?) scholars in each field. There is really no space to publish meta-scientific papers that "shake up" the field and call out established researchers, as editors that publish those pieces could come under similar criticism for their work. I think that it is not an accident that the discussion of the replication crisis in psychology started from blog posts and other non-academic avenues and then found its way to more "established" publications in the field (again, if I remember the context of those conversations).
I really wish that the review process was open. It would be interesting to see the reviewers comments to this specific paper and how the editor decided to pick up and engage with them. All those conversations are usually locked up in some editorial management system and are seldom made public. I don't know if we can really have open science without having open peer reviews.
Similarly, the replication crisis was being discussed in a lot of areas, especially in psychology, throughout this time, but was largely ignored until after the Bem ESP study. Registered replications aren't new, nor is concern about meta-science; it's just had renewed focus in recent years for various reasons.
It's not all that surprising to me that meta-science is associated with psychology. After all, not only is psychology often sort of fuzzy (by necessity of its subject matter), but it's the science of human behavior, which I think can lay claim to scientist behavior as well.
I think it's arguably the greatest contribution of psychology to the sciences in general.
In a sense we do have this: engineering and finance. Engineering turns good hard science into new tools, machines and weapons, and Finance turns good (predictive) soft science into new ways to make money.
In the long run it usually comes out, but the run can be longer than you think, and you may not be where you think in it with regard to any particular current theory. I wonder what things we know all "know" are proven by science will be dismissed by later generations. (I personally guess a lot of genetics-related stuff will be).
(Note that something doesn't need to be verifiable, reliable, or true to be "actionable". You can act on anything...)
I think this is a common critique, but I also think it is missing the point. What if the question of interest isn't so easily verifiable like in Engineering? Do we just throw up our hands and give up on those questions? [The alternative to good social science is not no social science, it’s bad social science](https://statmodeling.stat.columbia.edu/2021/03/12/the-social...).
Finance is also a bit tautological in this regard. It seems that often prediction models are impossible to disprove (e.g., our arbitrage method doesn't work anymore, the market updated). Yes good for putting skin in the game, but doesn't seem like it does much to advance our long-term understanding of humans.
Some things may well be complex enough that it's simply impossible, with the amount of resources available to the average university, to conduct a thorough enough study on a representative enough sample that accounts for enough confounding factors to make a statistically sound prediction that generalises. If this were the case for a significant proportion of the subjects of study of a particular field, then it might well be better to "give up" and admit we don't and cannot know, otherwise we're essentially creating a factory for bad science (as the available resources relative to the scope of the problem aren't sufficient to create good science, and there's no negative feedback to stop the bad science).
We can bikeshed what makes something a "science" till the cows come home but the philosophy of science and epistemology were not settled with Bacon and Popper - the end goal has always understanding in the broadest sense. Those studies have value as long as they help someone make sense of and adapt to the social systems they're in. It does mean though that those studies should be approached with extreme caution (see the decades wasted on string theory) and anyone basing their research off past results needs to carefully validate their assumptions.
[1] I think in this case "predictive" as a scientific term of art is too restricting. Social sciences often deal with very personal interactions that appear nondeterministic at the scale of a society but are relatively predictable when applied to a stereotypical office or school setting.
At the very least, it seems to me like the person I originally responded to would also disagree with judging social sciences for its "utility" - the article they linked specifically contrasted it with the natural sciences that "solve problems".
Closest analogy off the top of my head is psychiatric drugs: their efficacy is generally bottom of the barrel except for some group with factor X (each drug has their own unique factor X). For the vast majority of these drugs, we have no method of screening for whether a person has factor X - we don't even know what it is most of the time - so doctors have to go through a process of trial and error with patients until they find the right drug or combination. Once they do, it's like a night and day difference for the patient, yet if we applied the same standard of evidence for psychiatric drugs that we do for blood pressure pills, we'd never make any progress. A lot of the drugs look like they don't work in phase III and we have no way to predict which drug which help which patient but the patients figure it out with their doctors because they have actionable data, even if it isn't predictive in general.
Also, the difference between a bad method and a good method, is that the good method makes more accurate, better calibrated predictions (that is, using it makes us better gamblers).
Another related discussion was about the grievance studies scandal, which also touches on peer review and academic rigor in journals: https://news.ycombinator.com/item?id=18127811
Engineering organizations inside major corporations usually actively engage in process improvement because they are resourced to do so.
I only had a good introduction to it when I took it as an optional course in a humanities college.
If that's going to happen, it has to come from outside the government-science complex.
Good luck with that...
Most science has very little market value.
Also dont want to point fingrers but some scientists come from places where cheating is the norm.
For example, here’s a scholarly article on the exact question you mention - how and where peer review came to be seen as a guarantor of scientific quality: https://www.journals.uchicago.edu/doi/abs/10.1086/700070 (tldr: it wasn’t the 17th century Royal Society; it’s much more recent.)
https://scholar.harvard.edu/files/shapin/files/shapin-pump_c...
RCTs are good when they can be done and I'm all for doing more of them and too often there's no good excuse for not doing them. But at some level things just get impractical.