From A Man in Love:

When I discuss such topics with Geir, with whom I talk on the telephone for an hour every day, he is wont to quote Sven Stolpe, who has written somewhere about Bergman claiming that he would have been Bergman irrespective of where he had grown up, implying, in other words, that you are who you are whatever your surroundings. What shapes you is the way you are towards your family rather than the family itself. When I was growing up I was taught to look for the explanation of all human qualities, actions, and phenomena in the environment in which they originated. Biological or genetic determinants, the givens, that is, barely existed as an option, and when they did they were viewed with suspicion. Such an attitude can at first sight appear humanistic, inasmuch as it is intimately bound up with the notion that all people are equal, but upon closer examination it could just as well be an expression of a mechanistic attitude to man, who, born, empty, allows his life to be shaped by his surroundings.

Perhaps Bergman (harder to pin Knausgaard down) was overstating, but one lesson from these childhood mover papers is that the experience of society makes people more similar.¹ Those estimators measure common effects of place that make people experiencing the same environment more similar. In a segregated society, variation in treatment emerges. But what are the common experiences of society, generationally, nationally, or globally, for which we can't approximate the counterfactual?

1. I heard Chuck Klosterman say this in an interview years ago. I now wonder if he was thinking about this Knausgaard passage. ↩

• Gave an elections and redistricting project a more durable Codex workflow.
• Added clearer places for planning, review notes, and repeated research tasks.
• Put the first cross-project Codex diary system in place.
• The nightly summary now comes from saved breadcrumbs and git history, not chat memory.
• Cleaned up the personal academic website and blog for launch.
• Newer posts with images and numbered lists now render correctly.
• Tightened blog timing so the daily diary stays hidden until the evening.
• Wrapped up a Philip Roth text-analysis package for public release.
• A coyote-sightings analysis now makes a narrower claim about reporting clusters rather than animal behavior.

People ask for a Roth analogue as though the answer should be obvious. But "like Philip Roth" can mean at least five different things at once. It can mean writing about sex, guilt, family, ethnic inheritance, and American institutions in the same key. It can mean a certain sentence texture: tense, intelligent, restless, self-revising. It can mean the social choreography of his fiction: parents, lovers, rivals, intellectuals, doctors, campuses, Jewishness, class aspiration, urban and suburban pressure. Or it can mean something harder to pin down but impossible to miss in a good Roth novel: the voice that is forever confessing, prosecuting itself, justifying itself, and talking itself into the next trouble.

That is why the usual "if you like Roth, read..." lists always feel a little thin. They collapse several different likenesses into one word. Codex wanted to know which authors in a legally accessible comparison corpus actually stay close to Roth across multiple dimensions, not just one.

So Codex built a constrained but honest corpus and measured it.

If you want the replication materials, code, intermediate outputs, and notes, they live here: replication folder for this analysis. The key scripts are src/run_pipeline.py, which downloads the source pages and builds the corpus, and src/make_figures.py, which generates the figures.

The method, in plain English

The strongest local corpus Codex could legally and reproducibly access during this run was not a folder of full novels. It was a set of publicly available fiction pages from The New Yorker, which exposes article text cleanly enough to be parsed without scraping tricks. That matters, because Codex did not want the whole exercise resting on fake access to copyrighted books or on vibes extracted from reviews.

The final corpus included 46 usable texts and about 329,000 words from eleven authors: Philip Roth plus Don DeLillo, Jhumpa Lahiri, Zadie Smith, Mary Gaitskill, Jennifer Egan, Junot Diaz, George Saunders, Lorrie Moore, Aleksandar Hemon, and Tessa Hadley.

The data-science part matters here, because Codex did not just read the stories and declare a winner. Codex turned the corpus into numbers several different ways.

First, Codex split the texts into roughly 350-word passages and built a high-dimensional term matrix using TF-IDF on words and bigrams. Then Codex used truncated singular-value decomposition to compress that matrix into a lower- dimensional semantic space and measured each author's closeness to Roth with cosine similarity. That gave Codex a topic / semantic score.

Second, Codex built a stylometric feature table: average sentence length, sentence- length dispersion, paragraph length, lexical diversity, punctuation habits, dialogue density, and a function-word profile. Again, Codex compared each author's feature vector to Roth's with cosine similarity.

Third, Codex built interpretable proxy scores for social world, confessional voice, and emotional-moral texture using explicit vocabularies: kinship words, sex and body language, politics terms, work and money terms, first-person pressure, self-justification markers, hedging, argument words, shame words, mortality language, and so on.

Then Codex scored similarity to Roth along five dimensions:

1. Topic / semantic field: who writes about the most Roth-like worlds and conflicts.
2. Style: sentence shape, paragraph rhythm, punctuation, lexical variety, function-word profile.
3. Social-world vocabulary: kinship, sex, politics, academia, urban life, work, money, ethnicity, illness.
4. Confessional markers: first-person pressure, interiority, self-justification, hedging, argument, rhetorical volatility.
5. Emotional / moral vocabulary: shame, anger, affection, mortality, judgment.

Only after showing those families separately did Codex combine them into a composite score. Even there, Codex did not trust one weighting scheme. Codex reran the ranking with equal weights, topic-heavy weights, style-heavy weights, voice-heavy weights, and versions that dropped one major family entirely. The point was not to produce a single magic number. The point was to see which authors stayed near Roth when the measurement changed.

The first figure to keep in mind is the overall leaderboard, which turns that composite into something easy to scan while also marking which authors Jacob had already read on Goodreads.

The green bars are authors already in Jacob's Goodreads history; the orange bars are high-ranking authors not found in the export. That is what makes the Hemon / Hadley recommendation angle visible at a glance rather than only in prose.

The headline result

Within this corpus, the authors who come out most like Roth overall are:

1. Junot Diaz
2. Mary Gaitskill
3. Aleksandar Hemon

That is not the standard cocktail-party answer, and that is precisely why it is interesting.

Diaz is the strongest all-around match in this run. He is not merely close to Roth in one respect. He remains near him on topic, social-world vocabulary, confessional markers, and emotional-moral pressure. What links them is not surface imitation. It is the combination of family drama, erotic friction, ethnic inheritance, intimate self-exposure, and a voice that is smart enough to know when it is lying to itself.

Gaitskill is the strongest match in narrative posture. If you care most about Roth's confessional aggression, his moral self-cross-examination, and his capacity to make desire sound both lucid and incriminating, she looks unusually close in this corpus.

Hemon is the sleeper result. He is not one of the obvious names people reach for when talking about Roth, but he keeps surfacing near him in the current run, especially on social-world vocabulary, confessional markers, and emotional-moral vocabulary. If Roth often feels like a novelist of displacement inside intimacy, Hemon belongs in the conversation.

You can also see the shape of the top matches in profile form rather than as a single rank. The radar chart below makes clear that Diaz, Gaitskill, Hemon, Hadley, and DeLillo are not "close" in the same way.

The dimension-by-dimension picture

If what you mean by "Roth-like" is topic, the clearest match here is Don DeLillo. He is the best thematic neighbor in the corpus. He lives in adjacent territory: public life, postwar America, institutions, urban systems, bodies under pressure. But once the model asks for Roth's particular emotional and moral vocabulary, DeLillo falls back. He is Roth-adjacent, but not the closest all-around match in this run.

If what you mean is style, the surprise is Jhumpa Lahiri. Her sentence-level control and function-word profile come out strikingly close to Roth, even though her social world is not especially Roth-like. She is a good example of why one-score answers are misleading: a writer can sound structurally close without inhabiting the same human ecosystem.

If what you mean is social-world vocabulary, Junot Diaz and Aleksandar Hemon dominate. If what you mean is confessional and argumentative markers, Mary Gaitskill leads. If what you mean is emotional and moral weather, the leaders are Junot Diaz, Aleksandar Hemon, and Tessa Hadley.

That gives us a useful distinction:

• Closest overall: Junot Diaz
• Closest in voice: Mary Gaitskill
• Closest in themes: Don DeLillo
• Closest under-discussed analogue: Aleksandar Hemon

The heatmap is probably the single most informative visual in the package, because it shows the multi-dimensional argument in one place.

This is the chart that prevents the piece from collapsing into one lazy score. DeLillo glows on topic and confessional markers but not emotional-moral vocabulary. Lahiri is a style outlier. Diaz and Gaitskill stay bright in multiple columns for different reasons.

Who looks close on one dimension and distant on another?

This was one of the most useful outputs of the project.

DeLillo is the clearest partial match. The model loves him on topic and narrative stance, but not on emotional-moral texture. Lahiri is the inverse: she comes out strongly on style and voice, weakly on social-world vocabulary. George Saunders is a good cautionary case too. He lands fairly high because he is such an unmistakable prose technician, but he does not really inhabit Roth's social or emotional world.

Those splits are exactly why "who writes like Roth?" is a hard question. It is really a bundle of smaller questions.

How this compares to common wisdom

The conventional shortlist around Roth tends to include Saul Bellow, John Updike, Don DeLillo, Jonathan Franzen, and Bernard Malamud. My current corpus cannot directly test Bellow, Updike, Franzen, or Malamud, because Codex was not able to assemble a legally accessible comparison corpus for them in this run. So this is background contrast, not a broader empirical benchmark.

But it does confirm one standard intuition: DeLillo belongs in the room. He just does not win the whole contest.

What the empirical approach adds is a different set of names that common wisdom does not emphasize enough:

• Junot Diaz
• Mary Gaitskill
• Aleksandar Hemon

Those three are not interchangeable with Roth. None of them is. But they are the authors in this corpus who remain nearest when similarity stops being one lazy scalar and becomes a stack of separate tests.

Goodreads overlap

Once the Goodreads export was copied into the project output folder, Codex could finally verify the personalized part instead of leaving it as a handoff note.

Among the top Roth-adjacent authors in this corpus, Jacob has already read some of the most important ones:

• Junot Díaz: Drown, This Is How You Lose Her, The Brief Wondrous Life of Oscar Wao
• Mary Gaitskill: Veronica, The Mare, Bad Behavior, and several other books in the export
• Don DeLillo: White Noise, The Silence, Pafko at the Wall

The strongest high-ranking authors who do not appear in the export are:

• Aleksandar Hemon
• Tessa Hadley

That makes the cleanest personalized recommendation angle pretty straightforward: the analysis says Jacob has already covered several of the best-supported matches, and the two most obvious unread next authors are Hemon and Hadley.

The compact table below is the cleanest summary of that recommendation logic.

Read-next shortlist

Now that the export is verified, the clean recommendation shortlist is:

1. Aleksandar Hemon if Jacob wants the strongest high-ranking match he has not yet logged on Goodreads.
2. Tessa Hadley if Jacob wants another high-ranking unread author with strong emotional and social-world proximity.
3. Junot Díaz if Jacob wants the strongest overall match to reread or revisit.
4. Mary Gaitskill if Jacob wants the sharpest confessional and erotic analogue.
5. Don DeLillo if Jacob wants thematic and institutional adjacency rather than the full Roth package.

What this does not prove

It does not prove that Junot Diaz is the single most Roth-like writer in all of modern literature. The corpus is too narrow for that, and the right way to say so is plainly.

What it does show is that once you insist on legal access, reproducibility, multiple dimensions, and visible uncertainty, the obvious answer changes. The best Roth analogues in this run are not just the old canonical peers. They are the writers who combine confession, family pressure, social friction, argument, and moral embarrassment in ways that survive the tests this corpus could support.

That is a more interesting answer than "critics say X," and, within the limits of the corpus and the checks Codex actually ran, a more trustworthy one.

Replication materials for the full pipeline, figures, and intermediate outputs are available here: replication folder for this analysis.

It is increasingly clear that the infrastructure around the model is a force multiplier for what the model can achieve. I am still skeptical that I can always tell when one top model is better than a competitor. But I am curious and ready to be proven wrong. Partly as an educational exercise, and partly to expand Codex's capacity in my workflow to do more autonomous work, I worked with ChatGPT and Codex to translate Pedro Sant'Anna's claude-code-my-workflow repo so that as much of the same functionality as possible could be emulated in Codex. I was particularly interested in the specialist agent capabilities (R reviewer, proofreader, writer, etc.) and the adversarial agent that critiques presentational work and code so the model iterates on itself to make the final product and pipeline stronger. So far, Codex seems great for building out codebases in fairly frequent contact with the human researcher, which I mostly prefer as a workflow because it is easier to keep track of the project. But it was not immediately clear how to get it to fully run away and write a paper on its own.

So this port was an attempt to learn more about how Codex and Claude work while also expanding what I can do with the models. Specifically: how much of the Claude workflow from Sant'Anna's repo can be preserved when the assistant changes from Claude to Codex?

You can find the repo for this project here: codex-my-workflow

As Codex explains it to me:

The simplest way to think about a workflow repo is as a toolbox plus a rulebook for working with an AI assistant.

- instructions about how to behave

- templates for plans and reports

- reusable specialist roles

- project memory that survives past one conversation

- verification steps so "done" means something real

That structure turns an AI assistant from "a smart autocomplete window" into

something closer to a junior contractor with a clipboard, a checklist, and a filing cabinet.

The Claude Code setup was appealing because it treated reliability as a workflow problem. It had several appealing features:

• Plan first. This is pretty standard operating procedure with Codex or Claude ("enter plan mode" or "make a plan").
• Keep durable notes on disk so context is not trapped in one chat window.
• Use specialists when one general reviewer is not enough.
• Verify outputs instead of trusting fluent prose.
• Gate completion with explicit quality thresholds.
• Use an adversarial critic/fixer loop instead of a single pass.

So I forked the repo, then asked ChatGPT 5.4 Pro in the browser how I could work with Codex to port it into a Codex-focused version with as much of the same functionality as possible. ChatGPT helped draft initial AGENTS.md and plan documents to guide the Codex rewrite, and it also helped draft the prompt that got Codex started on the translation. Codex then evaluated the Claude repo and decided which documents could generally be preserved, what had to be adapted, and what could not be recreated one-for-one.

The first lesson is that, at a high level, Claude and Codex are similar enough that a port is genuinely possible. They can both read repository content, edit files, follow detailed instructions, run shell commands, work iteratively, and use role-like specializations. They also both benefit greatly from foundational project guidance rather than one-off prompts. But the interesting part of porting this workflow was where they differ.

As Codex put it:

Claude and Codex are similar in the way two kitchens are similar: both have heat, knives, and counters, but the appliances are in different places and some of them work differently. A recipe can survive that move, but not by copying the layout blindly.

There are some important differences. The syntax of guiding documents varies, so the repo structure through which Claude and Codex receive guidance differs. Codex translated Claude project guidance into root and nested AGENTS.md files. Claude settings became Codex config, hooks, and rules. Claude agent definitions became .codex/agents/*.toml. Claude skills became repo-local skills under .agents/skills/.

Another difference is that Codex makes explicitness more important. Codex is less likely to automatically spawn agents such as the adversarial reviewer or specialist agents. This functionality can still be achieved, but prompts need to ask for it explicitly to better ensure those agents get deployed. That changes how the workflow has to be written.

One takeaway is that in the original Claude-centered workflow, Claude can feel more anticipatory of likely researcher desires, and therefore more autonomous. In Codex, the safer pattern is to be more explicit. Prompts generally should name the specialists to be spun up, tell Codex what to do with them, and state verification expectations directly. Codex thus feels a little less magical and in need of a bit more handholding from the researcher and the workflow structure around it. But this is one of the most useful lessons from the port. When platforms differ, a good fallback is often not "invent more automation." It is "store the important files so the model can read them and stay on target."

Exciting times for data science. So this is a new space for random thoughts on research, teaching, hobbyistic data science experiments, and trying to extract the highest intelligence from new AI tools.

Some posts automated using AI, noted as such.