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Record W4362241787 · doi:10.1353/ner.2023.0013

Portrait of the Technocrat as a Stanford Man

2023· article· en· W4362241787 on OpenAlexaboutno aff
Shaan Sachdev

Bibliographic record

VenueNew England review · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicLiterature, Film, and Journalism Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPortraitArt historyWifeRomanceArtVisual artsEngineeringLawHistoryLiteraturePolitical science

Abstract

fetched live from OpenAlex

Portrait of the Technocrat as a Stanford Man Shaan Sachdev (bio) The first time it snowed this year, I cried. Snow is one of my favorite things in the world—and it’s one of the Stanford man’s, too. He was surely trekking through it as well, somewhere in downtown Brooklyn. But unlike me, trudging mournfully to my office in the same pair of jeans I’d worn all week, the Stanford man would be crunching through the ice and salt in top-of-the-line hiking boots and a $1,900 jacket. He’d be filtering the flakes through fleece-lined leather gloves from Burberry and then returning to his glass apartment on the fortieth floor of a luxury building to shower with Molton Brown soaps. He’d greet the doorman on his way in, one of six, and it wouldn’t matter which was on duty, because he’d know all their names. He’d make cordial conversation with whomever was in the elevator, the trim blond pushing the stroller or the paunchy techie with the Labrador, because exchanging pleasantries is also one of his favorite things. When the Stanford man broke up with me, he told me it was because he needed to date someone more “average.” He told me that intellectualizing and reading tomes were for graduate school, not the stuff of romance. He told me that ordinary pleasures, like watching basketball and dancing to thumping techno, were to be savored, not scrutinized. He reminded me, I realized, of Jim Barnett, the subject of Mary McCarthy’s “Portrait of the Intellectual as a Yale Man,” from her 1942 novel The Company She Keeps. “If other people on the left stood in superstitious awe of Jim,” McCarthy wrote: Jim also stood in awe of himself. It was not that he considered that he was especially brilliant or talented; his estimation of his qualities was both just and modest. What he reverenced in himself was his intelligent mediocrity. He knew that he was the Average Thinking Man to whom in the end all appeals are addressed. . . . He was a walking Gallup Poll, and he had only to leaf over his feelings to discover what America was thinking. Like Jim Barnett, who could have been a model if he’d fallen on hard times, the Stanford man is approached now and then by scouts and agents. He is tall, with [End Page 95] a bronze complexion, thick, curly hair, and lips like pillows. He is clean but not in the ordinary, courteous sort of way. He’s so clean that it’s the first thing one notices about him. His skin glows and his clothes are spotless and everything he wears looks new. One can’t possibly imagine any odor wafting from his person. Even after exercising, the fabric of his premium gym clothes seems to, as advertised, absorb all efflux. He brushes five, sometimes six times a day, always for twice as long as the American Dental Association recommends, erasing all evidence of delicately prepared breakfast smoothies, sumptuous tenderloin dinners, and eighteen-dollar craft cocktails. Most evenings, after the Stanford man finishes speaking on conference calls and making spreadsheets on his computer—the things that propel his six-figure salary—he eats dinner with friends. Sometimes with just one friend, sometimes with five. Sometimes he and his roommates prepare fresh poke bowls in their globule above the city, only faintly registering the puny, glittering Statue of Liberty out their living room windows as they chop and chatter. Sometimes he sits at a table in Chelsea with a horde of other twenty-somethings who have also attended Stanford—or Harvard, Princeton, or Yale. They wear silver TAG Heuer wristwatches and dark blue cashmere sweaters, and they mostly exchange anecdotes about things that happened to them in South America or about companies that mutual friends are starting. Serious moments might entail ruminations upon spin class, air miles, stock prices, or cryptocurrencies. None of them, it ought to be said, are unintelligent. In fact, their minds are put to regular labor. They can tell you which health insurance plans come with the best digital applications. They can tell you...

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.359
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.244
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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