Field-weighted citation impact
← Most-used equations and models · All bibliometrics equations
A paper's citations divided by the average for papers of the same field, year and document type.
LaTeX: FWCI_i = \frac{c_i}{\bar c_{f, y, d}}
Variables and notation#
c_i is an item's citation count and c-bar_(f,y,d) the expected count for its field, publication year and document type; FWCI is their ratio.
Worked example#
A paper with 12 citations against an expected count of 4 has FWCI=3, or three times the benchmark.
Related methods and when to use them#
FWCI normalizes citation context; raw citations do not. Results depend on database, field assignment and citation window; an expected value of zero needs explicit handling and small samples can be volatile.
Sources and further reading#
Key findings#
- Field-weighted citation impact was mentioned in 2 papers in the last 30 days (2026-10-07 to 2026-11-05), ranking #214 of the 248 equations and models mentioned.
- By journal category: Higher Education and Research (2).
- Most of them appeared in Scientometrics (2).
- Often mentioned together with Journal impact factor (2), Regression discontinuity design (1) and Instrumental variables (two-stage least squares) (1).
- From Nov 2023 to Oct 2026, 76 archived papers mentioned Field-weighted citation impact in their title, abstract, keywords or full text, most of them in Jul 2026 (6).
- From 2017 to 2026 (so far), Field-weighted citation impact was mentioned in 87 papers in the tracked journals, most of them in higher education and research (65), management (5) and statistics (4).
- 23 of them were published in 2025, against 1 in 2017.
- Since 2023, the institutions with the most of these papers were University of Technology Sydney (6), Max Planck Society (4) and Mercatorum University (4).
- The journals publishing most of them were Scientometrics (43), Journal of Informetrics (9) and Higher Education (4).
- The most-cited of them is “Comparison of two article-level, field-independent citation metrics: Field-Weighted Citation Impact (FWCI) and Relative Citation Ratio (RCR)” (Journal of Informetrics, 2019), with 188 citations.
Mentions over 36 months#
| Trend | Nov 23–Oct 24 | Nov 24–Oct 25 | Nov 25–Oct 26 | 36 months |
|---|---|---|---|---|
| 18 | 22 | 36 | 76 |
Papers per year#
20172026 (so far)
| Category | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 (so far) |
|---|---|---|---|---|---|---|---|---|---|---|
| Higher Education and Research | 0 | 0 | 1 | 0 | 0 | 3 | 4 | 16 | 18 | 23 |
| Management | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 |
| Statistics | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 1 |
| Economics | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 |
| Information Systems | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 |
| Operations Research | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| Health Economics and Policy | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| AI | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 |
| Tourism and Hospitality | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| All categories | 1 | 1 | 1 | 1 | 0 | 3 | 6 | 18 | 23 | 33 |
Where the papers come from#
| # | Journal | Papers |
|---|---|---|
| 1 | Scientometrics | 43 |
| 2 | Journal of Informetrics | 9 |
| 3 | Higher Education | 4 |
| 4 | Quality & Quantity | 3 |
| 5 | Research Policy | 2 |
| 6 | The Journal of Academic Librarianship | 2 |
| 7 | Journal of the Knowledge Economy | 2 |
| 8 | Metrika | 1 |
| 9 | Minerva | 1 |
| 10 | Technology in Society | 1 |
| # | Institution | Papers |
|---|---|---|
| 1 | University of Technology Sydney | 6 |
| 2 | Max Planck Society | 4 |
| 3 | Mercatorum University | 4 |
| 4 | Universidad Complutense de Madrid | 4 |
| 5 | Universidad Politécnica de Madrid | 4 |
| 6 | University of Rome Tor Vergata | 4 |
| 7 | German Centre for Higher Education Research and Science Studies | 3 |
| 8 | University of Sheffield | 3 |
| 9 | Aarhus University | 2 |
| 10 | Adam Mickiewicz University in Poznań | 2 |
| # | Country | Papers |
|---|---|---|
| 1 | Spain | 14 |
| 2 | Germany | 9 |
| 3 | United States | 9 |
| 4 | Australia | 8 |
| 5 | United Kingdom | 8 |
| 6 | Italy | 7 |
| 7 | Netherlands | 5 |
| 8 | Poland | 5 |
| 9 | Hungary | 4 |
| 10 | Austria | 3 |
| # | Keyword | Papers |
|---|---|---|
| 1 | citation impact | 21 |
| 2 | research evaluation | 16 |
| 3 | research productivity | 12 |
| 4 | citation metrics | 11 |
| 5 | bibliometric indicators | 10 |
| 6 | field-weighted citation impact | 10 |
| 7 | research collaboration | 8 |
| 8 | Scopus | 7 |
| 9 | bibliometric analysis | 6 |
| 10 | disciplinary differences | 6 |
Most-cited papers#
| Date | Title | Journal | Authors | Affiliations |
|---|---|---|---|---|
| 2026-10-08 | Metric capital: how academic indicators become interactional resource | Scientometrics | Łukasz Remisiewicz | University of Gdańsk |
| 2026-10-08 | Are large language models able to predict highly cited papers? Evidence from statistical publications | Scientometrics | Zhanshuo Ye, Yiming Hou, Rui Pan et al. | Central University of Finance and Economics; Peking University |
Cite this page
Scholar4AI. (2026). Field-weighted citation impact. Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/equations/field-weighted-citation-impact
@misc{scholar4ai-equations-field-weighted-citation-impact,
author = {{Scholar4AI}},
title = {Field-weighted citation impact},
year = {2026},
howpublished = {\url{https://scholar4ai.com/equations/field-weighted-citation-impact}},
urldate = {2026-11-05},
note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}