Key indicators summarise the report’s central metrics. All values are calculated from the underlying dataset and refer to the full period unless otherwise stated. Percentages (peer-reviewed, Open Access, international collaboration) are calculated as a share of total publications per year.
Percentage change is not shown when the base value is below 10 units, as small base values produce statistically unstable percentages (Hicks et al. (2015), principle 8; cf. CDC rule for n < 16). Absolute values are shown instead.
Insights
During the period 2011-2024, the
compound annual growth rate (CAGR) was +0.4%. The trend is
increasing (not statistically significant).
Mann
(1945);
Sen
(1968)
Insights
The peer-reviewed share increased from
52.9 % to 100% (+47.1 percentage points) during 2011–2024.
315 publications (98%) scientific, 8 publications (2%) other.
Older years (2011–2016) aggregated for readability. Full timespan available in data export.
The Norwegian Publication Indicator (NPI), also known as the Norwegian list, classifies publication channels into two levels. Level 2 (top ~20% per field) is considered the most prestigious channels. Level 1 covers other approved channels.
DORA mode is enabled for this report. Bifrost evaluates the report against the principles of DORA (2012) and CoARA (2022).
The report contains elements that are not compatible with DORA principle 1: “Do not use journal-based metrics as a surrogate measure of the quality of individual research articles.”
NPI level classification (Level 1/2) is shown in the report. NPI is a Nordic classification system that ranks publication channels by academic standing, i.e. a channel-based ranking that DORA advises against using as a quality surrogate. The classification is presented here as descriptive information about publication patterns, not as a measure of individual article quality.
Researchers are listed below, sorted by scientific productivity.
Insights
The top 20% most productive researchers
account for 32% of publications (Gini 0.14, scale: 0 = even, 1 = fully
concentrated). Average number of co-authors increased from 1.4
(2011–2017) to 1.7 (2018–2024).
Insights
12 research groups of roughly equal
size; no single cluster dominates. Each researcher collaborates with an
average of 1.5 others (a sparsely connected network). Clear cluster
structure (Modularity 0.91); researchers primarily work within their own
group. The network is sparse: only 4.9% of all researcher pairs have a
direct collaboration link.
Each node represents a researcher and each link a co-authorship. Colors indicate research groups (clusters) identified via modularity analysis. Node size reflects number of publications.
The co-authorship network is built from co-authored publications. Each node represents a researcher, and each edge is weighted by number of joint publications. Edge weights are normalized using association strength Van Eck et al. (2009) before clustering with the Louvain algorithm. Centrality measures: degree (number of collaborators), collaboration intensity (total co-authoring frequency), and bridge score (weighted betweenness using inverse weights) Newman (2004). Network density measures the proportion of realized vs. possible collaborations. Terminology: «Collaborators (avg)» = mean degree; «Clustering» = modularity Blondel et al. (2008).
Percentages are calculated on pairs where both authors have country data (58 classified of 58 total, coverage 100%).
‘Co-authored texts’ indicates the number of texts the author has written together with one or more co-authors.
Network statistics show central nodes in the collaboration network. Degree is the number of direct collaborations, while betweenness shows which authors act as bridges between different groups.
The first co-author listed is the one the author has written with the most times. ‘Number’ indicates the number of co-authored texts with the author. Up to four additional co-authors are listed, in descending order of co-authorship.
Below is a visualization of the 12 different groupings in the dataset. The colors indicate different groups.
Adaptive visualization (large): 31 nodes / 23 edges shown in full (no filtering needed).
The visualization was restored to the unfiltered source network: the adaptive filters would otherwise have reduced it below the threshold for a meaningful display.
Author names to the right; group ID to the left. You can see the size of the groups and the most common keywords of the groups in the tables that follow. A combination of search and sorting can be used to further explore group membership.
The table is limited to a) groups with more than 3 members; b) groups with at least one keyword in any publication; c) the ten most used keywords per group.
See also the supervisor and opponent network in the researcher section for an analysis of academic collaboration patterns beyond co-authorship.
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80 institutions contribute. Karlstads
universitet, Helsingfors universitet and Göteborgs universitet account
for 22 % — a broad distribution.
Overview of international collaboration based on co-authorship and affiliations in publications.
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14 countries represented in
collaborations. Norway, Finland and Denmark are most common. 4 % of
publications involve international co-authors — down from 6 %
(2011–2017) to 1 % (2018–2024).
Based on co-author affiliation country.
Insights:
The network comprises 12 institutions
with 9 collaboration relationships. The strongest collaboration is
between Aalborg universitet and Norwegian University of Science and
Technology (2 co-publications). Linnaeus University has the most
collaboration partners (3).
Insights
Social Sciences dominates (87 %).
Subject breadth has decreased (2011–2024, H: 0.47 → 0.37). Moderate
interdisciplinarity — research combines related subject areas.
Rao-Stirling: 0.318 (where 0 = single discipline, 1 = maximum
diversity). Based on 4 HSV main areas (Swedish classification).
Rao-Stirling
(Stirling, 2007)
Shannon H (evenness index) measures how evenly publications are distributed across subject areas. A value of 1.00 means perfect evenness; lower values indicate dominance by individual areas. Rao-Stirling measures interdisciplinarity by weighing both the distribution and the taxonomic distance between subject areas according to the Swedish classification system. The scale ranges from 0 (all publications in one subject) to 1 (maximum spread across distant subject areas).
Proportion of total publications per year (%). Note that a publication may belong to multiple categories.
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## v1) — recomputing.During 2011–2024, 2.8% (9 of 323) publications lack subject classification at this level.
Proportion of total publications per year (%). Note that a publication may belong to multiple categories.
religious studies and theology; lärande; learning; geovetenskap(ersätts med naturgeografi)
Insights
Broad keyword profile — no single term
dominates (HHI: 0.0029 — Herfindahl-Hirschman Index, where 0 = perfectly
even distribution, 1 = one term dominates entirely). Most common is
“religious education” appearing in 11.1 % of publications, across a
total of 972.
didactics; education; geografi; historiedidaktik; historieundervisning; history teaching; religion; samhällskunskap; social studies; subject-specific education; ämnesdidaktik
Colors indicate frequency quantiles within this dataset.
Red: Highest frequency (11.1-8.94%); Blue: High frequency (8.94-6.78%); Green: Medium frequency (6.78-4.62%); Orange: Low frequency (4.62-2.46%); Gray: Lowest frequency (2.46-0.3%)
The dataset contains 323 publications. A lower correlation threshold (|r| ≥ 0.4) is used to identify potential trends in smaller datasets.
Potentially rising: curriculum, utbildning och lärande
Potentially declining: religious education
Comparison of relative frequency (share of publications) between periods 2011–2021 and 2022–2024.
Deep-dive analysis is shown for keywords with at least 6 publications in the focus period. The threshold follows Pearson correlation’s requirement of at least four degrees of freedom (n‑ 2 ≥ 4).
A growing trend may indicate increased research interest, but can also reflect terminological shifts.
New keywords (absent 2011–2021): historical significance, religious literacy, affective significance, agency, content
The following keywords show strong growth compared to the base period, which may indicate increasing research interest.
Growth period: 2018–2024
Driving actors (growth) (growth period 2018–2024):
Researchers: Ammert, Niklas (3), Dessingué, Alexandre (3), Nolgård, Olle (2), Alvén, Fredrik (1), Axinder, Emma (1)
Institutions: Linnéuniversitetet (5), Universitetet i Stavanger (4), Uppsala universitet (4), Universitetet i Amsterdam (3), Linköpings universitet (2)
Co-occurring keywords: subject-specific education, ämnesdidaktik, curriculum
Last 3 years: 8 publications
Growth period: 2017–2023
Driving actors (growth) (growth period 2017–2023):
Researchers: Tväråna, Malin (3), Andersson, Klas (2), Larsson, Kristoffer (2)
Institutions: Göteborgs universitet (4), Uppsala universitet (4), Stockholms universitet (3)
Co-occurring keywords: ämnesdidaktik, civics, samhällskunskap
Last 3 years: 4 publications
Analysis based on a limited number of publications (n = 11). Results should be interpreted with caution.
Growth period: 2018–2022
Driving actors (growth) (growth period 2018–2022):
Researchers: Enstedt, Daniel (2), Nygren, Thomas (2), Wickström, Johan (2)
Institutions: Uppsala universitet (14), Göteborgs universitet (2)
Co-occurring keywords: religious education, subject-specific education, critical religion
Last 3 years: 7 publications
Analysis based on a limited number of publications (n = 8). Results should be interpreted with caution.
Growth period: 2017–2024
Driving actors (growth) (growth period 2017–2024):
Researchers: Khawaja, Amna (2), Engren, Jimmy (1), Gestsdóttir, Súsanna Margrét (1)
Institutions: Helsingfors universitet (3), Universitetet i Amsterdam (3), Göteborgs universitet (1)
Co-occurring keywords: history education, historical literacy, historical significance
Last 3 years: 3 publications
Analysis based on a limited number of publications (n = 7). Results should be interpreted with caution.
Growth period: 2019–2024
Driving actors (growth) (growth period 2019–2024):
Researchers: Dessingué, Alexandre (2), Alvén, Fredrik (1), Bergum Johanson, Lisbeth (1)
Institutions: UiT Norges arktiske universitet (2), Universitetet i Stavanger (2), Høgskulen i Volda (1)
Co-occurring keywords: history culture, history education, alta-saken
Last 3 years: 2 publications
Analysis based on a limited number of publications (n = 7). Results should be interpreted with caution.
Showing the 5 keywords with strongest statistical evidence out of 21 identified. Selection is based on significance (p < 0.05) and correlation strength (Kendall’s tau).
Other growing keywords (lower statistical evidence): religionsdidaktik (+50%, n=7), social science education (+50%, n=7), textbooks (+50%, n=7), comparative subject didactics (+650%, n=3), geografiundervisning (+650%, n=3), lived religion (+650%, n=3), social studies education (+650%, n=3), visual literacy (+650%, n=3), discourse analysis (+462%, n=5), ethics education (+462%, n=5), knowledge (+275%, n=4), moral education (+275%, n=4), primary school (+275%, n=4), religionsundervisning (+275%, n=4), undervisning (+275%, n=4), historical literacy (+150%, n=5)
No statistically significant rising keywords were identified. Below are keywords with strong correlations (|r| > 0.5) that may indicate emerging trends, but do not reach statistical significance (p < 0.05). With a small dataset, these trends may be real but statistically uncertain.
Some keywords in this section also appear under Rapidly growing keywords. This is expected: rapid relative growth and statistical significance are complementary measures, not synonyms.
The following keywords show a steady decline over the entire time period, without having had a distinct burst period previously:
A declining trend may reflect reduced research interest or a shift to newer terminology for the same research area.
Early publishers (2012–2015):
Researchers: Hylén, Torsten (2), Aarnio-Linnanvuori, Essi (1), Bråten, Oddrun M.H. (1)
Most active period (2017–2019, 13 publications):
Researchers: Aldrin, Emilia (1), Aldrin, Viktor (1), Andersen, Kirsten M. (1)
Institutions: Umeå universitet (2), Bath Spa University (1), Göteborgs universitet (1)
Co-varying keywords: subject-specific education, ämnesdidaktik, social studies
Last 3 years: 7 publications
Note: A declining trend may indicate terminological shift rather than decreased research interest.
The heatmap shows how often keywords co-occur in the same publications. Association strength Van Eck et al. (2009) normalizes co-occurrence by the product of the individual keyword frequencies. Red asterisks (✱) in the upper-right corner of cells mark statistically significant co-occurrences (p < 0.05).
Keywords that frequently co-occur in the same publications form thematic clusters. The table below summarizes the clusters; the interactive graph shows the relationships visually.
The network diagram shows how keywords relate to each other based on co-occurrence in publications. Larger nodes mean more frequent keywords. Lines show co-occurrence. Colors indicate thematic clusters identified using the Leiden algorithm (Traag et al., 2019).
The frequency of individual words in the dataset as a whole. Words have been taken from title, abstract, and keywords. “Frequency” is total uses, including the number of mentions in the same text, while “publications” is the number of unique texts where the word appears.
Insights
The 5 most common words (by share of
publications) are: “education” (77%), “subject” (53%), “school” (51%),
“teaching” (42%), “students” (42%). These patterns reflect the thematic
core of the dataset.
Here is a summary of word stems, i.e. parts of words, in the dataset as a whole. Words have been taken from title, abstract, and keywords. “Frequency” is total uses, including the number of mentions in the same text, while “publications” is the number of unique texts where the word appears.
These trends are indicative and complement the keyword analysis above.
Methodological note: Word frequency analysis is based on individual words extracted from title, abstract, and keywords. Unlike author-selected keywords, individual words can be noisier and more ambiguous — for example, the word ‘system’ may appear in both technical and social science contexts, while the keyword ‘adaptive systems’ is more precise. Stricter thresholds are used (minimum 10 occurrences, correlation > 0.5) and academic stopwords are excluded.
Rising/declining shows trends over time (Spearman correlation). New/disappearing shows lifecycle — when words started or stopped being used.
No statistically significant trends were identified among the most common words.
The OA analysis is based on 3 publications with DOI matched against OpenAlex (1% of 323 total). 320 publications lack DOI and are therefore not included in OA statistics.
Note:
3 of 3 publications with DOI were matched
against OpenAlex and assigned OA status (100%). OA status is sourced
from OpenAlex (based on Unpaywall). OA status may be retroactively
classified — a publication that is freely available today may have been
closed at the time of publication. The trend should therefore be
interpreted with caution, especially for older publications.
A complete list of the search results. Initially sorted by year (descending) and author (ascending). Change the order at the column header. Search can be done over all displayed fields.
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