Evidence map›Paper›PMID 36712938›Full record

ArticlePNAS nexus2023

A deep-learning model of prescient ideas demonstrates that they emerge from the periphery.

Paul Vicinanza, Amir Goldberg, Sameer B Srivastava

Erratum issuedAbstract read
In one paragraph

Article in PNAS nexus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Judicial hierarchy and discursive influence.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2024
    Article
  2. Minority-group incubators and majority-group reservoirs support the diffusion of climate change adaptations.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Paul VicinanzaGraduate School of Business, Stanford University, 655 Knight Way, Stanford, CA 94305, USA.ORCID https://orcid.org/0000-0003-3681-3380
Amir GoldbergGraduate School of Business, Stanford University, 655 Knight Way, Stanford, CA 94305, USA.ORCID https://orcid.org/0000-0002-0858-3058
Sameer B SrivastavaHaas School of Business, University of California, Berkeley, 2220 Piedmont Ave, Berkeley, CA 94720, USA.ORCID https://orcid.org/0000-0001-8793-0793

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Where do prescient ideas-those that initially challenge conventional assumptions but later achieve widespread acceptance-come from? Although their outcomes in the form of technical innovation are readily observed, the underlying ideas that eventually change the world are often obscured. Here, we develop a novel method that uses deep learning to unearth the markers of prescient ideas from the language used by individuals and groups. Our language-based measure identifies prescient actors and documents that prevailing methods would fail to detect. Applying our model to corpora spanning the disparate worlds of politics, law, and business, we demonstrate that it reliably detects prescient ideas in each domain. Moreover, counter to many prevailing intuitions, prescient ideas emanate from each domain's periphery rather than its core. These findings suggest that the propensity to generate far-sighted ideas may be as much a property of contexts as of individuals.

Identifiers

PMID36712938
PMCPMC9832965

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.