Example social construction of knowledge in physics: the speed of light

The graph below shows historical estimates of the speed of light, c, alongside uncertainty intervals (Klein & Roodman, 2005, Figure 1). The horizontal line shows the currently agreed value, now measured with high precision.

Note the area I’ve pointed to with the pink arrow, between 1930 and 1940. These estimates are around 17km/sec too slow relative to what we know now, but with relatively high precision (narrow uncertainty intervals). Some older estimates were closer! What went wrong? Klein and Roodman (2005, p.143) cite a post-mortem offering a potential explanation:

“the investigator searches for the source or sources of […] errors, and continues to search until he [sic] gets a result close to the accepted value.

“Then he [sic] stops!”

Fantastic case study illustrating the social construction of scientific knowledge, even in the “hard” sciences.

References

Klein, J. R., & Roodman, A. (2005). Blind analysis in nuclear and particle physics. Annual Review of Nuclear and Particle Science, 55, 141–163. doi: 10.1146/annurev.nucl.55.090704.151521 [preprint available]

Dedekind on natural numbers

The “standard model” of arithmetic is the idea you probably have when you think about natural numbers (0, 1, 2, 3, …) and what you can do with them. So, for instance, you can keep counting as far you like and will never run out of numbers. You won’t get a struck in a loop anywhere when counting: the numbers don’t suddenly go 89,90, 91, 80, 81, 82, … Also 2 + 2 = 4, x + y = y + x, etc.

One of the things mathematicians do is take structures like this standard model of arithmetic and devise lists of properties describing how it works and constraining what it could be. You could think of this as playing mathematical charades. Suppose I’m thinking of the natural numbers. How do I go about telling you what I’m thinking without just saying, “natural numbers” or counting 0, 1, 2, 3, … at you? What’s the most precise, unambiguous, and concise way I could do this, using principles that are more basic or general?

Of the people who gave this a go for the natural numbers, the most famous are Richard Dedekind (1888, What are numbers and what should they be?) and Giuseppe Peano (1889, The principles of arithmetic, presented by a new method). The result is called Peano Arithmetic or Dedekind-Peano Arithmetic. What I find interesting about this is where the ideas came from. Dedekind helpfully explained his thinking in an 1890 letter to Hans Keferstein. A chunk of it is quoted verbatim by Hao Wang, (1957, p. 150). Here’s part:

“How did my essay come to be written? Certainly not in one day, but rather it is the result of a synthesis which has been constructed after protracted labour. The synthesis is preceded by and based upon an analysis of the sequence of natural numbers, just as it presents itself, in practice so to speak, to the mind. Which are the mutually independent fundamental properties of this sequence [of natural numbers], i.e. those properties which are not deducible from one another and from which all others follow? How should we divest these properties of their specifically arithmetical character so that they are subsumed under more general concepts and such activities of the understanding, which are necessary for all thinking, but at the same time sufficient, to secure reliability and completeness of the proofs, and to permit the construction of consistent concepts and definitions?”

Dedekind spelt out his list of properties of what he called a “system” of N. Key properties are as follows (this is my paraphrase except where there is quoted text; also I’m pretending Dedekind started the numbers at zero when he actually started at one):

  1. N consists of “individuals or elements” called numbers.
  2. Each element of N is related to others by a relation (now called the successor), intuitively, “the number which succeeds or is next after” a number. But remember that we don’t have “next after” in this game. The successor of an element of N is another element of N. This captures part of the idea of counting along the numbers.
  3. If two numbers are distinct, then their successors are also distinct. So you can’t have say, the successor of 2 as 3 and also the successor as 4 as 3.
  4. Not all elements of N are a successor of any element.
  5. In particular, zero isn’t a successor of any element.

Dedekind notes that there are many systems that satisfy these properties and have N as a subset but also have arbitrary “alien intruders” which aren’t the natural numbers:

“What must we now add to the facts above in order to cleanse our system […] from such alien intruders […] which disturb every vestige of order, and to restrict ourselves to the system N? […] If one assumes knowledge of the sequence N of natural numbers to begin with and accordingly permits himself an arithmetic terminology, then he has of course an easy time of it. […]”

But we aren’t allowed to use arithmetic to define arithmetic. Dedekind explains again the intuitive idea of a number being in N if and only if you can get to it by starting at 0 and working along successors until you reach that number. This he formalises as follows:

  1. An element n belongs to N if and only if n is an element of every system K such that (i) the element zero belongs to K and (ii) the successor of any element of K also belongs to K.

So, we get the number 0 by 6(i), the number 1 by 6(ii) since it’s the successor of 0, the number 2 by applying successor to 1, and so on until an infinite set of natural numbers is formed. This approach is what we now call mathematical induction.

There are a few issues with Dedekind-Peano Arithmetic, though – for another time…

Drawing an is-ought

Hume’s (1739) Treatise famously argued that we cannot infer an “ought” from an “is”. This has presented an enduring problem for science: how should we produce a set of recommendations for what should be done following the results of a study? If a new cancer treatment dramatically improves remission rates, should study authors simply shrug, present the results, and leave the recommendations to politicians? What if a treatment causes significant harms – can we recommend that the treatment be banned? Or suppose we have ideas for future studies that should be carried out and want to summarise them in the conclusions…? Even doing this would be ruled out by Hume.

The solution, if it is one, is that any recommendations require a set of premises stating our values. These values necessarily assert something beyond the evidence, for instance that if a treatment is effective then it should be provided by the health service. In practice, such values are often left implicit and assumed to be shared with readers. But there are interesting examples where it is apparently possible to draw an is-ought inference without assuming values.

One example, due to Mavrodes (1964), begins with the premise

If we ought to do A, then it is possible to do A.

This seems reasonable enough. It would, for instance, be horribly dystopian to require that people behave a particular way if it were impossible for them to do so. Games like chess and tennis have rules that are possible – if they were impossible then it would make playing the games challenging. Let’s see what happens if we apply a little logic to this premise.

Sentences of the form

If A, then B

are equivalent to those of the contrapositive form

If not-B, then not-A

This can be seen in the truth table below, where 1 denotes true and 0 denotes false. The values of the last two columns are equivalent:

A B not-A not-B If A, then B If not-B, then not-A
1 1 0 0 1 1
1 0 0 1 0 0
0 1 1 0 1 1
0 0 1 1 1 1

Together, this means that if we accept the premise

If we ought to do A, then it is possible to do A,

and the rules of classical logic, we must also accept

If it is not possible to do A, then it is not the case that we ought to do A.

But here we have an antecedent that is an “is” and a consequent that is an “ought”: logic has licenced an is-ought!

Worry not: there has been debate in the literature… See Gillian Russell (2021) for a recent analysis.

References

Mavrodes, G. I. (1964). “Is” and “Ought.” Analysis, 25(2), 42–44.

Russell, G. (2021). How to Prove Hume’s Law. Journal of Philosophical Logic. In press.

The value of high quality qualitative research

Here’s an interesting paper (Greenland & Moore, 2021) that used our (Fugard & Potts, 2015) quantitative model for choosing a sample size for a thematic analysis. The authors also had a probability sample – very rare to see in published qualitative research.

Key ingredients: they had a sample frame (students who dropped out of open online university courses and their phone numbers); they wanted a comprehensive typology of reasons for drop out and suggestions for retaining students; and they could complete each interview within an average of 15 minutes (emphasis on average: some must have been longer).

Here are the authors’ conclusions:

“This study’s research design demonstrates the value of using a larger qualitative probability-based sample, in conjunction with in-depth interviewer probing and thematic analysis to investigate non-traditional student dropouts. While prior qualitative research has often used smaller samples (Creswell, 2007), recent studies have highlighted the need for more rigorous sample design to enable subthemes within themes, which is the key purpose of thematic analysis (eg, Nowell et al., 2017). This study’s sample moved beyond simple thematic saturation rationale, with consideration of the level of granularity required (Vasileiou et al., 2018). That is, 226 participants had a 99% probability of capturing all relevant dropout reason subthemes, down to a 5% incidence level or frequency of occurrence (Fugard & Potts, 2015). This study therefore presents a definitive typology of non-traditional student dropout in open online education.”

It’s exciting to see a rigorous and yet pragmatic qualitative study.

References

Fugard, A. J. B. & Potts, H. W. W. (2015). Supporting thinking on sample sizes for thematic analyses: A quantitative toolInternational Journal of Social Research Methodology, 18, 669-684. (There’s an app for that.)

Greenland, S. J., & Moore, C. (2021). Large qualitative sample and thematic analysis to redefine student dropout and retention strategy in open online education. British Journal of Educational Technology.

Being realistic about “realist” evaluation

Realist evaluation (formerly known as realistic evaluation; Pawson & Tilley, 2004, p. 3) is an approach to Theory-Based Evaluation that treats, e.g., burglars and prisons as real as opposed to narrative constructs (that seems uncontroversial); follows “a realist methodology” that aims for scientific “detachment” and “objectivity”; and also strives to be realistic about the scope of evaluation (Pawson & Tilley, 1997, pp. xii-xiv).

“Realist(ic)” evaluation proposes something apparently new and distinctive. But how does it look in practice? What’s new about it? Let’s have a read of Pawson and Tilley’s (1997) classic to try to find out.

Déjà vu

Open any text on social science methodology, and it will say something like the following about the process of carrying out research:

  1. Review what is known about your topic area, including theories which attempt to explain and bring order to the various disparate findings.
  2. Use prior theory, supplemented with your own thinking, to formulate research questions or hypotheses.
  3. Choose methods that will enable you to answer those questions or test the hypotheses.
  4. Gather and analyse data.
  5. Interpret the analysis in relation to the theories introduced at the outset. What have you learned? Do the theories need to be tweaked? For qualitative research, this interpretation and analysis are often interwoven.
  6. Acknowledge limitations of your study. This will likely include reflection about whether your method or the theory are to blame for any mismatch between theory and findings.
  7. Add your findings to the pool of knowledge (after a gauntlet of peer review).
  8. Loop back to 1.

Realist evaluation has similar:

Figure 4.1 and 4.2 from Pawson and Tilley (1997), glued together for ease of comparison. The left loop is taken from a 1970s text on sociological method and the right loop is the authors’ revision for “realist” evaluation.

It is scientific method as usual with constraints on what the various stages should include for a study to be certified genuinely “realist”. For instance, the theories should be framed in terms of contexts, mechanisms, and outcomes (more on which in a moment); hypotheses emphasise the “for whom” and circumstances of an evaluation; and instead of “empirical generalisation” there is a “program specification”.

The method of data collection and analysis can be anything that satisfies this broad research loop (p. 85):

“… we cast ourselves as solid members of the modern, vociferous majority […], for we are whole-heartedly pluralists when it comes to the choice of method. Thus, as we shall attempt to illustrate in the examples to follow, it is quite possible to carry out realistic evaluation using: strategies, quantitative and qualitative; timescales, contemporaneous or historical; viewpoints, cross-sectional or longitudinal; samples, large or small; goals, action-oriented or audit-centred; and so on and so forth. [… T]he choice of method has to be carefully tailored to the exact form of hypotheses developed earlier in the cycle.”

This is reassuringly similar to the standard textbook story. However, like the standard story, in practice there are ethical and financial constraints on method meaning that the ideal approach to answer a question may not be feasible, and yet an evaluation of some description is deemed necessary nonetheless. Indeed the UK government’s evaluation bible, the Magenta Book (HM Treasury, 2020), recommends using Theory-Based approaches like “realist” evaluation when experimental and quasi-experimental approaches are not feasible. (See also, What is Theory-Based Evaluation, really?)

More than a moment’s thought about theory

Pawson and Tilley (1997) emphasise the importance of thinking about why social interventions may lead to change and not only looking at outcomes, which they illustrate with the example of CCTV:

“CCTV certainly does not create a physical barrier making cars impenetrable. A moment’s thought has us realize, therefore, that the cameras must work by instigating a chain of reasoning and reaction. Realist evaluation is all about turning this moment’s thought into a comprehensive theory of the mechanisms through which CCTV may enter the potential criminal’s mind, and the contexts needed if these powers are to be realized.” (p. 78)

They then list a range of potential mechanisms. CCTV might make it more likely that thieves are caught in the act. Or maybe the presence of CCTV make car parks feel safer, which means they are used by more people whose presence and watchful eyes prevent theft. So other people provide the surveillance rather than the camera bolted to the wall.

Nothing new here – social science is awash with theory (Pawson and Tilley cite Durkheim’s 1950s work on suicide as an example). Psychological therapies are some of the most evaluated of social interventions and the field is particularly productive when it comes to theory; see, e.g., Whittle (1999, p. 240) on psychoanalysis, a predecessor of modern therapies:

“Psychoanalysis is full of theory. It has to be, because it is so distrustful of the surface. It could still choose to use the minimum necessary, but it does the opposite. It effervesces with theory…”

To take a more contemporary example, Power (2010) argues that effects in modern therapies involve at least one of the following three activities: exploring and using how the relationship between therapist and client mirrors relationships outside therapy (transference); graded exposure to situations which provoke anxiety; and challenging dysfunctional assumptions about how the social world works. For each of these activities there are detailed theories of change.

However, perhaps evaluations of social programmes – therapies included – have concentrated too much on tracking outcomes and neglected getting to grips with testing potential mechanisms of change, so “realist” evaluation is potentially a helpful intervention. The specific example of CCTV is a joy to read and is a great way to bring the sometimes abstract notion of  social mechanism alive.

The structure of explanations in “realist” evaluation

Context-mechanism-regularity (or outcome) – the organisation of explanation in “realist” evaluations

The context-mechanism-outcome triad is a salient feature of the approach. Rather than define each of these (see the original text), here are four examples from Pawson and Tilley (1997) to illustrate what they are. The middle column (New mechanism) describes the putative mechanism that may be “triggered” by a social programme that has been introduced.

Context New mechanism Outcome
Poor-quality, hard-to-let housing; traditional housing department; lack of tenant involvement in estate management Improved housing and increased involvement in management create increased commitment to the estate, more stability, and opportunities and motivation for social control and collective responsibility Reduced burglary
prevalence
Three tower blocks, occupied mainly by the elderly; traditional housing department; lack of tenant involvement in estate management Concentration of elderly tenants into smaller blocks and natural wastage creates vacancies taken up by young, formerly homeless single people inexperienced in independent living. They become the dominant group. They have little capacity or inclination for informal social control, and are attracted to a hospitable estate subterranean subculture Increased burglary prevalence concentrated amongst the more
vulnerable; high levels of vandalism and incivility
Prisoners with little or no previous education with a growing string of convictions – representing a ‘disadvantaged’ background Modest levels of engagement and success with the program trigger ‘habilitation’ process in which the inmate experiences self-realization and social acceptability (for the first time) Lowest levels of reconviction as compared with statistical norm for such inmates
High numbers of prepayment meters, with a high proportion of burglaries involving cash from meters Removal of cash meters reduces incentive to burgle by decreasing actual or perceived rewards Reduction in percentage of burglaries involving meter breakage; reduced risk of burglary at dwellings where meters are removed; reduced burglary rate overall

This seems a helpful way to organise thinking about the context-mechanism-outcome triad, irrespective of whether the approach is labelled “realist”. Those who are into logframe matricies (logframes) might want to add a column for the “outputs” of a programme.

The authors emphasise that the underlying causal model is “generative” in the sense that causation is seen as

“acting internally as well as externally. Cause describes the transformative potential of phenomena. One happening may well trigger another but only if it is in the right condition in the right circumstances. Unless explanation penetrates to these real underlying levels, it is deemed to be incomplete.” (p. 34)

The “internal” here appears to refer to looking inside the “black box” of a social programme to see how it operates, rather than merely treating it as something that is present in some places and absent in others. Later, there is further elaboration of what “generative” might mean:

“To ‘generate’ is to ‘make up’, to ‘manufacture’, to ‘produce’, to ‘form’, to ‘constitute’. Thus when we explain a regularity generatively, we are not coming up with variables or correlates which associate one with the other; rather we are trying to explain how the association itself comes about. The generative mechanisms thus actually constitute the regularity; they are the regularity. The generative mechanisms thus actually constitute the regularity; they are the regularity.” (p. 67)

We also learn that an action is causal only if its outcome is triggered by a mechanism in a context (p. 58). Okay, but how do we find out if an action’s outcome is triggered in this manner? “Realist” evaluation does not, in my view, provide an adequate analysis of what a causal effect is. Understandable, perhaps, given its pluralist approach to method. So, understandings of causation must come from elsewhere.

Mechanisms can be seen as “entities and activities organized in such a way that they are responsible for the phenomenon” (Illari & Williamson, 2011, p. 120). In “realist” evaluation, entities and their activities in the context would be included in this organisation too – the context supplies the mechanism on which a programme intervenes. So, let’s take one of the example mechanisms from the table above:

“Improved housing and increased involvement in management create increased commitment to the estate, more stability, and opportunities and motivation for social control and collective responsibility.”

To make sense of this, we need a theory of what improved housing looks like, what involvement in management and commitment to the estate, etc., means. To “create commitment” seems like a psychological, motivational process. The entities are the housing, management structures, people living in the estate, etc. To evidence the mechanism, I think it does help to think of variables to operationalise what might be going on and to use comparison groups to avoid mistaking, e.g., regression to the mean or friendlier neighbours for change due to improved housing. And indeed, Pawson and Tilley use quantitative data in one of the “realist” evaluations they discuss (next section). Such operationalisation does not reduce a mechanism to a set of variables; it is merely a way to analyse a mechanism.

Kinds of evidence

Chapter 4 gives a range of examples of the evidence that has been used in early “realist” evaluations. In summary, and confirming the pluralist stance mentioned above, it seems that all methods are relevant to realist evaluation. Two examples:

  1. Interviews with practitioners to try to understand what it is about a programme that might effect change: “These inquiries released a flood of anecdotes, and the tales from the classroom are remarkable not only for their insight but in terms of the explanatory form which is employed. These ‘folk’ theories turn out to be ‘realist’ theories and invariably identify those contexts and mechanisms which are conducive to the outcome of rehabilitation.” (pp. 107-108)
  2. Identifying variables in an information management system to “operationalize these hunches and hypotheses in order to identify, with more precision, those combinations of types of offender and types of course involvement which mark the best chances of rehabilitation. Over 50 variables were created…” (p. 108)

Some researchers have made a case for and carried out what they term realist randomised controlled trials (Bonell et al., 2012; which seems eminently sensible to me). The literature subsequently exploded in response. Here’s an illustrative excerpt of the criticisms (Marchal et al., 2013, p. 125):

“Experimental designs, especially RCTs, consider human desires, motives and behaviour as things that need to be controlled for (Fulop et al., 2001, Pawson, 2006). Furthermore, its analytical techniques, like linear regression, typically attempt to isolate the effect of each variable on the outcome. To do this, linear regression holds all other variables constant “instead of showing how the variables combine to create outcomes” (Fiss, 2007, p. 1182). Such designs “purport to control an infinite number of rival hypotheses without specifying what any of them are” by rendering them implausible through statistics (Campbell, 2009), and do not provide a means to examine causal mechanisms (Mingers, 2000).”

Well. What to make of this. Yes, RCTs control for stuff that’s not measured and maybe even unmeasurable. But you can also measure stuff you know about and see if that moderates or mediates the outcome (see, e.g., Windgassen et al., 2016). You might also use the numbers to select people for qualitative interview to try to learn more about what is going on. The comment on linear regression reveals surprising ignorance of how non-linear transformations of and interactions between predictors can be added to models. It is also trivial to calculate marginal outcome predictions for combinations of predictors together, rather than merely identifying which predictors are likely non-zero when holding others fixed. See Bonell et al. (2016) for a very patient reply.

Conclusions

The plea for evaluators to spend more time developing theory is welcome – especially in policy areas where “key performance indicators” and little else are the norm (see also Carter, 1989, on KPIs as dials versus tin openers opening a can of worms). It is a laudable aim to help “develop the theories of practitioners, participants and policy makers” of why a programme might work (Pawson & Tilley, 1997, p. 214). The separation of context, mechanism, and outcome, also helps structure thinking about social programmes (though there is widespread confusion about what a mechanism is in the “realist” literature; Lemire et al., 2020). But “realist” evaluation is arguably better seen as an exposition of a particular reading of ye olde scientific method applied to evaluation, with a call for pluralist methods. I am unconvinced that it is a novel form of evaluation.

References

Bonell, C., Fletcher, A., Morton, M., Lorenc, T., & Moore, L. (2012). Realist randomised controlled trials: a new approach to evaluating complex public health interventions. Social Science & Medicine, 75(12), 2299–2306.

Bonell, C., Warren, E., Fletcher, A., & Viner, R. (2016). Realist trials and the testing of context-mechanism-outcome configurations: A response to Van Belle et al. Trials, 17(1), 478.

Carter, N. (1989). Performance indicators: “backseat driving” or “hands off” control? Policy & Politics, 17, 131–138.

HM Treasury (2020). Magenta Book.

Illari, P. M., & Williamson, J. (2011). What is a mechanism? Thinking about mechanisms across the sciencesEuropean Journal for Philosophy of Science2(1), 119–135.

Lemire, S., Kwako, A., Nielsen, S. B., Christie, C. A., Donaldson, S. I., & Leeuw, F. L. (2020). What Is This Thing Called a Mechanism? Findings From a Review of Realist Evaluations. New Directions for Evaluation, 167, 73–86.

Marchal, B., Westhorp, G., Wong, G., Van Belle, S., Greenhalgh, T., Kegels, G., & Pawson, R. (2013). Realist RCTs of complex interventions – an oxymoron. Social Science & Medicine, 94, 124–128.

Pawson, R., & Tilley, N. (1997). Realistic Evaluation. SAGE Publications Ltd.

Pawson, R., & Tilley, N. (2004). Realist evaluation. Unpublished.

Power, M. (2010). Emotion-focused cognitive therapy. London: Wiley.

Whittle, P. (1999). Experimental Psychology and Psychoanalysis: What We Can Learn from a Century of Misunderstanding. Neuropsychoanalysis1, 233-245.

Windgassen, S., Goldsmith, K., Moss-Morris, R., & Chalder, T. (2016). Establishing how psychological therapies work: the importance of mediation analysis. Journal of Mental Health, 25, 93–99.

So, you have pledged allegiance to critical realism – what next?

So, you have pledged allegiance to the big four critical realist axioms (Archer, et al., 2016) – what next?

Here are some ideas.

1. Ontological realism

What is it? There is a social and material world existing independently of people’s speech acts. “Reality is real.” One way to think about this slogan in relation to social kinds like laws and identities is they have a causal impact on our lives (Dembroff, 2018). Saying that reality is real does not mean that reality is fixed. For example, we can eat chocolate (which changes it and us) and change laws.

What to do? Throw radical social constructionism in the bin. Start with a theory that applies to your particular topic and provides ideas for entities and activities to use and possibly challenge in your own theorising.

Those “entities” (what a cold word) may be people with desires, beliefs, and opportunities (or lack thereof) who do things in the world like going for walks, shopping, cleaning, working, and talking to each other (Hedström, 2005). The entities may be psychological “constructs” like kinds of memory and cognitive control and activities like updating and inhibiting prepotent responses. The entities might be laws and activities carried out by the criminal justice system and campaigners. However you decide to theorise reality, you need something.

How an intervention may influence someone’s actions by influencing their desires, beliefs, and/or opportunities (Hedström, 2005, p. 44)

2. Epistemic relativity

What is it? The underdetermination of theories means that two theorists can make a compelling case for two different accounts of the same evidence. Their (e.g., political, moral) standpoint and various biases will influence what they can theorise. Quantitative researchers are appealing to epistemic relativity when they cite George Box’s “All models are wrong” and note the variety of models that can be fit to a dataset.

What to do? Throw radical positivism in the bin – even if you are running RCTs. Ensure that you foreground your values whether through statements of conflicts of interest or more reflexive articulations of likely bias and prejudice. Preregistering study plans also seems relevant here.

There may be limits to the extent to which an individual researcher can articulate their biases, so help out your colleagues and competitors.

3. Judgemental/judgmental rationality

What is it? Even though theories are underdetermined by evidence, there often are reasons to prefer one theory over another.

What to do? If predictive accuracy does not help choose a theory, you could also compare them in terms of how consistent they are with themselves and other relevant theories; how broad in scope they are; whether they actually bring some semblance of order to the phenomena being theorised; and whether they make novel predictions beyond current observations (Kuhn, 1977).

You might consider the aims of critical theory which proposes judging theories in terms of how well they help eliminate injustice in the world (Fraser, 1985). But you would have to take a political stance.

4. Ethical naturalism

What is it? Although is does not imply ought, prior ought plus is does imply posterior ought.

What to do? Back to articulating your values. In medical research the following argument form is common (if often implicit): We should prevent people from dying; a systematic review has shown that this treatment prevents people from dying; therefore we should roll out this treatment. We could say something similar for social research that is anti-racist, feminist, LGBTQI+, intersections thereof, and other research. But if your research makes a recommendation for political change, it must also foreground the prior values that enabled that recommendation to inferred.

In summary

The big four critical realist axioms provide a handy but broad metaphysical and moral framework for getting out of bed in the morning and continuing to do social research. Now we are presented with further challenges that depend on grappling with substantive theory and specific political and moral values. Good luck.

References

Archer, M., Decoteau, C., Gorski, P. S., Little, D., Porpora, D., Rutzou, T., Smith, C., Steinmetz, G., & Vandenberghe, F. (2016). What is Critical Realism? Perspectives: Newsletter of the American Sociological Association Theory Section, 38(2), 4–9.

Dembroff, R. (2018). Real talk on the metaphysics of gender. Philosophical Topics, 46(2), 21–50.

Fraser, N. (1985). What’s critical about critical theory? The case of Habermas and gender. New German Critique35, 97-131.

Kuhn, T. S. (1977). Objectivity, Value Judgment, and Theory Choice. In The Essential Tension: Selected Studies in Scientific Tradition and Change (pp. 320–339). The University of Chicago Press.

Hedström, P. (2005). Dissecting the social: on the principles of analytic sociology. Cambridge University Press.

Qual and quant – subjective and objective?

“… tensions between quantitative and qualitative methods can reflect more on academic politics than on epistemology. Qualitative approaches are generally associated with an interpretivist position, and quantitative approaches with a positivist one, but the methods are not uniquely tied to the epistemologies. An interpretivist need not eschew all numbers, and positivists can and do carry out qualitative studies (Lin, 1998). ‘Quantitative’ need not mean ‘objective’. Subjective approaches to statistics, for instance Bayesian approaches, assume that probabilities are mental constructions and do not exist independently of minds (De Finetti, 1989). Statistical models are seen as inhabiting a theoretical world which is separate to the ‘real’ world though related to it in some way (Kass, 2011). Physics, often seen as the shining beacon of quantitative science, has important examples of qualitative demonstrations in its history that were crucial to the development of theory (Kuhn, 1961).”

Fugard and Potts (2015, pp. 671-672)