Cited by 15Discourse Analysis in Language StudiesPsychology Research and BibliometricsAcademic Writing and Publishing

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

More confident, less formal: stylistic changes in academic psychology writing from 1970 to 2016

Overview

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Crossref · 10.1007/s11192-021-04166-9

Cites

5 of 5 references verified0 citation errors · 0 not checkable

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Citations

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Research works printed but not linked: 24

  1. Abola, M. V., & Prasad, V. (2016). The use of superlatives in cancer research. JAMA Oncology, 2, 139–141. · DOI
  2. Alluqmani, A., & Shamir, L. (2018). Writing styles in different scientific disciplines: A data science approach. Scientometrics, 115, 1071–1085. · DOI
  3. American Psychological Association. (2009). Publication manual of the American Psychological Association (6th ed.). APA Books.
  4. American Psychological Association. (2001). Publication manual of the American Psychological Association (5th ed.). APA Books.
  5. Duncan, S. Y., Chohan, R., & Ferreira, J. J. (2019). What makes the difference? Employee social media brand engagement. Journal of Business and Industrial Marketing, 34(7), 1459–1467. · DOI
  6. Eysenbach, G. (2011). Can tweets predict citations? Metrics of social impact based on twitter and correlation with traditional metrics of scientific impact. Journal of Medical Internet Research, 13(4), e123. · DOI
  7. Furnham, A. (2021). Publish or perish: Rejection, scientometrics and academic success. Scientometrics, 126(1), 843–847. · DOI
  8. González-Álvarez, J., & Cervera-Crespo, T. (2019). Contemporary psychology and women: A gender analysis of the scientific production. International Journal of Psychology, 54(1), 135–143. · DOI
  9. Haslam, N., Vylomova, E., Murphy, S. C., & Wilson, S. J. (2021). The neuroscientification of psychology: Rising prevalence of neuroscientific concepts in psychology from 1965–2016. Perspectives on Psychological Sciences. https://doi.org/10.1177/1745691621991864 · DOI
  10. Huang, W., Wang, P., & Wu, Q. (2018). A correlation comparison between Altmetric Attention Scores and citations for six PLOS journals. PLoS One, 13(4), e0194962. · DOI
  11. Hundt, M., & Mair, C. (1999). “Agile” and “uptight” genres: The corpus-based approach to language change in progress. International Journal of Corpus Linguistics, 4, 221–242. · DOI · Paper page
  12. Hyland, K. (2004). Disciplinary discourses, Michigan classics ed.: Social interactions in academic writing. University of Michigan Press. · DOI
  13. Hyland, K., & Jiang, F. (2017). Is academic writing becoming more informal? English for Specific Purposes, 45, 40–51. · DOI
  14. Kacewicz, E., Pennebaker, J. W., Davis, M., Jeon, M., & Graesser, A. C. (2014). Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology, 33(2), 125–143. · DOI
  15. Miller, G. (1969). Psychology as a means of promoting human welfare. American Psychologist, 24, 1063–1075. · DOI
  16. Oliver, K. M., Houchins, J. K., Moore, R. L., & Wang, C. (2021). Informing makerspace outcomes through a linguistic analysis of written and video-recorded project assessments. International Journal of Science and Mathematics Education, 19(2), 333–354. · DOI
  17. Pennebaker, J. W., Boyd, R. L., Jordan, K., & Blackburn, K. (2015). The development and psychometric properties of LIWC2015. University of Texas at Austin.
  18. Pennebaker, J. W., Mehl, M. R., & Niederhoffer, K. (2003). Psychological aspects of natural language use: Our words, our selves. Annual Review of Psychology, 54, 547577. · DOI · Paper page
  19. Seoane, E. (2013). On the conventionalisation and loss of pragmatic function of the passive in Late Modern English scientific discourse. Journal of Historical Pragmatics, 14(1), 70–99. · DOI
  20. Swales, J., & Feak, C. B. (2010). From text to task: Putting research on abstracts to work. Utrecht Studies in Language and Communication, 22, 167–180.
  21. Teich, E., & Fankhauser, P. (2010). Exploring a corpus of scientific texts using data mining. Language and Computers, 71, 233–247.
  22. Vaughn, L. A., Vazire, S., & Corker, K. (2019). Distinguishing between need support and regulatory focus with LIWC. Collabra: Psychology, 5(1), 32. · DOI
  23. Vinkers, C. H., Tijdink, J. K., & Otte, W. M. (2015). Use of positive and negative words in scientific PubMed abstracts between 1974 and 2014: Retrospective analysis. BMJ, 351, h6467. · DOI · Paper page
  24. Vymolova, E., Murphy, S., & Haslam, N. (2019). Evaluation of semantic change of harm-related concepts in psychology. In Proceedings of the 1st international workshop on computational approaches to historical language change. Association for Computational Linguistics, Pp. 29–34. · DOI

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Metadata

Authors
Wheeler, M. A., Vylomova, E., McGrath, M. J., & Haslam, N.
Affiliations
  • Swinburne University of Technology (AU)
  • The University of Melbourne (AU)
Corresponding author
Wheeler, Melissa A.
Venue
Scientometrics
Published
2021-09-26
Volume
126
Issue
12
Pages
9603-9612
ISSN
  • 0138-9130 (ISSN-L)
  • 1588-2861
OpenAlex
W3204737403
Cited by
15
References
5
Citations by year
2026: 2 · 2025: 4 · 2024: 3 · 2023: 2 · 2022: 3 · 2021: 1
FWCI
21.64
Type
journal article
Language
en
Open access
closed
Locations
  • Scientometrics (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Figshare (submittedVersion)
Funders
Australian Research Council (DP210103984)
Topics
  • Social Sciences › Linguistics and Language › Discourse Analysis in Language Studies
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Psychology Research and Bibliometrics
  • Social Sciences › Library and Information Sciences › Academic Writing and Publishing
Keywords
academic writing style, first person plural, clout, language norms, scientific writing, publication pressure, emotional tone, analytical thinking, authenticity

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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