Kevin Haggerty is a well-known criminologist at the University of Alberta. Long CV. Many graduate students. A serious name in that world.
He also appears to have committed a small act of academic mischief. Calling the act juvenile would miss the more interesting provocation, though the temptation is there. His response addressed a Qualitative Inquiry article that argued against the use of generative AI in reflexive qualitative research.
Readers unfamiliar with reflexive qualitative research need not pause over the term. The basic point is that the original article defended forms of qualitative inquiry where interpretation, subjectivity, and power are the work.
Haggerty produced a rebuttal, and, unless one noticed the coda, the most important detail was easy to miss. As the convention allows, he placed an asterisk beside his name. The note attached to that mark reveals that the piece was generated by AI in response to a prompt asking the system to think through the strengths and weaknesses of the Jowsey article.
Funny, yes. The joke is not the whole thing.
This tactic is now approaching a genre of academic writing in itself: the AI-generated response to the article that has just rejected AI. The move could be repeated with almost any academic article. One can imagine a new kind of research note whose main function is to say, in effect, this is an AI criticism of your position.
The joke, if there is one, concerns the fact that AI can produce a response that knows how to sound like this kind of response. The balanced reconstruction, the concession paragraph, the turn toward social justice, the warning against purity, and the concluding call for reflexive engagement all appear in recognizable order.
The original article was endorsed by 419 experienced qualitative researchers from 32 countries. I will call it the Jowsey article after the first author. That number of signatures makes the article, among other things, a disciplinary line in the sand. The authors argue that generative AI should be rejected for work such as reflexive thematic analysis, a way of coding, and phenomenology, a philosophical import into the social sciences and, depending on the room, either profound or vague. Choose your room carefully.
The authors do not argue that AI should never appear anywhere near qualitative research. Their target is Big Q qualitative work: reflexive thematic analysis, phenomenology, ethnography, and discourse analysis. In these approaches, interpretation, subjectivity, and power are part of the analytic object.
Their case is clear enough.
The premise can be reduced to a few claims. AI cannot make meaning. Reflexive qualitative analysis is a distinctly human practice. Large language models are tied to exploitative labour, environmental damage, and extractive infrastructures. At this point the familiar conference-room machinery begins to hum.
The objections are not trivial. One could argue that they are among the better objections to AI in research. If qualitative inquiry is centrally concerned with situated interpretation and attentiveness to power, as many social scientists argue, then handing analysis to an increasingly sophisticated text generator that operates by pattern recognition creates a real problem.
Haggerty’s reply asks whether this defence of qualitative inquiry depends on believing too much about qualitative inquiry.
The deeper provocation, then, is not simply that AI can imitate academic prose. Much academic social science may be easier to imitate than many practitioners want to admit.
The reason is simple.
Much academic social science is, in practice, a New Yorker essay with citations.
Some readers will hear that as an insult, although the New Yorker publishes many excellent essays. Good long-form nonfiction can observe social life, build a compelling interpretation, and accomplish much of what good social science claims to do.
Much of the best interpretive social science works in a similar fashion. The article identifies a pattern, gives that pattern a conceptual name, situates the concept in the literature, introduces data, usually interviews or some other form of qualitative material, and then explains why the pattern matters.
No shame follows from that sequence.
The problem begins when this essayistic activity insists on being treated as science in the same sense as pharmacology, epidemiology, materials research, or other fields that build cumulative, testable, and materially consequential knowledge.
The university uses the word research to cover wildly different activities: discovering a drug mechanism, estimating a policy effect, interpreting a novel, taking up an archive, interviewing 24 people about professional identity, or redescribing everyday life through the vocabulary of neoliberalism, coloniality, discourse, or precarity.
These are intellectual activities, but they are not the same activity.
I am not claiming that interpretive social science lacks value. The issue is that interpretive social science often borrows the prestige of science and research while operating according to the standards of sophisticated cultural criticism. When challenged, the work leans on method. When pressed for prediction or durable findings, the defence often retreats to a critique of the scientific method itself, or further back, into the dark shelter of complexity and situated interpretation.
Sometimes that retreat is justified. Human beings are complex. Social life is not a chemistry lab. Institutions, including universities, are historically layered. Meaning matters, context matters, and power matters. Much worth knowing cannot be reduced to variables.
If that is the defence of the social sciences, then the defence should be made clearly. Much of this work is interpretive nonfiction, disciplined by scholarly conventions worth keeping. The work is different rather than lesser.
What, then, should one make of Haggerty’s AI-generated response?
I doubt he would endorse my reading in full. Still, his response presses on the difference between interpretation and the genre markers of interpretation.
A great deal of academic writing in the social sciences has become recognizable by its moves. There is the gesture toward complexity, the refusal of binaries, the vocabulary of power, situatedness, and marginality, the ethical throat clearing, and the production of themes. Inevitably, something is under-theorized. Inevitably, the analysis complicates this or that.
Here is the rub. Generative AI is good at this kind of thing because this kind of thing is highly patterned.
That does not mean AI can do good qualitative research. Some qualitative research, and some styles of academic writing, have become formulaic enough that AI can produce a plausible counterfeit.
For that reason, the claim that only humans make meaning is both true and insufficient.
Only humans make meaning in the full existential, embodied, morally accountable sense. AI, to my knowledge, has no childhood, shame, body, field site, political courage, memory of being humiliated in a seminar room, grief for an unfinished life, risk, or obligation to participants.
The machine does not understand.
Human researchers, however, are not automatically profound because they are human. Humans produce cliches, impose theories on data, confuse moral vocabulary with moral insight, and mistake disciplinary training for sophisticated perception.
Here the argument becomes more interesting, and perhaps weaker. The Jowsey article does more than warn researchers against outsourcing interpretation to the machine. The article says GenAI is inappropriate in all phases of reflexive qualitative analysis, including initial coding. That is a larger claim, and a prohibition rather than a caution.
The more interesting use of AI here is not, “please analyze my interviews.” The more interesting use is, “show me the default reading.” Show me the most predictable interpretation. Show me what the machine thinks counts as a theme. Then the human researcher has to ask whether the analysis is doing anything better than that. Am I seeing something situated, risky, and ethically answerable, or am I producing the discipline’s default grammar with better feelings?
Reflexivity is supposed to address this problem, but reflexivity too often becomes a credential. The researcher declares positionality, names power, and, because of that little spiritual exercise, becomes authorized to proceed. In weaker work, reflexivity functions like a priestly indulgence. The sin of interpretation is forgiven because the interpreter has confessed.
Haggerty’s joke works because he refuses to grant the human analyst automatic sanctity.
Qualitative researchers who want to reject AI in reflexive analysis should avoid romanticizing the human. They should specify what forms of judgement cannot be outsourced: accountability, field knowledge, contextual understanding, interpretive risk, and the parts of analysis that make the researcher answerable for something.
That would be a stronger argument.
The AI question, then, becomes a mirror. The mirror asks what qualitative researchers do beyond producing plausible interpretive prose.
A better answer would be this: we make situated judgements under conditions of ethical responsibility. We attend to participants, contexts, histories, concepts, and contradictions. We show our work. We remain answerable for the interpretation. We distinguish evidence from atmosphere.
We earn the language of our supposed insights.
That standard is demanding.
Haggerty’s AI response does not prove that AI can do qualitative inquiry. The response proves that AI can perform a recognizable academic defence of qualitative inquiry. That may be the more embarrassing result.
A New Yorker piece with citations is not a shameful thing to be. I have failed many times to reach that standard myself, and it remains a shining light on the hill for me.
If much academic social science is a New Yorker piece with citations, then perhaps the work should be judged accordingly: by the quality of its insight, the honesty of its evidence, the freshness of its perception, and the precision of its concepts.
That standard may be higher than the one currently being used.
I keep circling the same problem from another direction in AI sycophancy as digital priesthood: confidence-shaped language can acquire authority before it has earned any.