Evidence map›Paper›PMID 36786282›Full record

ArticleAccountability in research2024

Letter to editor: Academic journals should clarify the proportion of NLP-generated content in papers.

Gengyan Tang

Abstract readLetterComment
PubMed Publisher
In one paragraph

Article in Accountability in research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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. Review
  2. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author.

Gengyan TangInstitute of Journalism and Communication, Sichuan Academy of Social Sciences.ORCID 0000-0003-3221-3134

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This letter to the editor argues that if academic journals are willing to accept papers that include NLP-generated content under certain conditions, editorial policies should clarify the proportion of NLP-generated content in the paper. Excessive use of NLP-generated content should be considered as academic misconduct.

Indexed as

Editorial PoliciesPeriodicals as TopicHumansPublishingScientific MisconductChatGPTMisconduct in researchNLP systemResearch ethicsResearch integrity

Identifiers

PMID36786282

What OpenQuestion holds

Textmetadata
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.