This page offers sample statistical analyses of the annotated Sappho fragments and the analysed reception testimonies. It opens with the reception indices, a composite value measuring the reception strength of individual texts. This is followed by average intertextual relations and shared phenomena, particularly dense intertextual relationships, and phenomena as the basis of intertextual relations. The further sections show all phenomena compared, their distribution by fragment reference, over time and by genre, as well as connections between components of plot variants, references to persons, references to works, and quotations. Finally, two sections are devoted to gender-specific analyses and to a popularity analysis using wiki metrics.
More information on the exemplary analysis can be found here.
A network visualization of all data is available here.
Simple frequency distributions of individual phenomena and listings of all intertextual relationships can be accessed via the »Reception Phenomena« tab (under »Analysis«). Frequency distributions of temporal and geographical focal points can be found in the individual catalogues via the »Reception Testimonies« tab (under »Texts«).
Statistics 1: Reception Indices
The reception index R(t) is a composite value on a scale from 0 (weak) to 1 (strong) indicating how intense
the measured reception is. The index combines two dimensions: phenomenon density P(t) – the total number of analytical units assigned to a text (references to persons,
places, and works, characters, rhetorical topoi, motifs, topics, plots or their variants,
and quotations) – and intertextual connectivity I(t), measured by the number of intertext nodes in which the text appears as the object.
Since phenomenon density follows a pronounced right-skewed distribution and intertextual
connectivity shows a weaker but similarly directed tendency, both raw values are log-transformed
(log(1 + x)); this scaling also reflects the assumption that the informational value of each
additional unit decreases as attestation density grows. The median of the texts analysed
as examples serves as the normalization anchor point; dividing by twice the log-transformed
median sets this anchor point to 0.5.
R(t) = 0,75 ·
Pnorm(t) + 0,25 · Inorm(t)
Phenomenon density is weighted at three-quarters, since it directly reflects the volume of content analysed; intertextual connectivity contributes the remaining quarter.
Statistics 2: Average Intertextual Relations and Shared Phenomena
On average, how many intertextual relations connect a text to others? And on average, how many phenomena does a text share with the texts it is intertextually connected to?
Statistics 3: Intertextual Relationships and Text Similarities
Which intertextual relations connect the most phenomena? This reveals which texts have the richest implicit similarities – independent of explicit references.
Statistics 4: Phenomena as the Basis of Intertextual Relations
Which phenomena are most often decisive for intertextual relations between Sappho fragments and reception testimonies, as well as between fragments and reception testimonies among themselves?
Phenomenon Types as a Basis for Intertextual Relationships
Co-occurrences of Individual Phenomena
The inner ring shows the phenomenon types, the outer ring the individual phenomena. Segment width indicates their frequency. The chords in the middle connect phenomena that particularly often occur together in intertextual relations – width and opacity scale with co-occurrence strength.
Most Frequent Phenomenon Combinations
Statistics 5: All Phenomena Compared
Which phenomena are actualized in Sappho fragments as well as in reception testimonies – and where are the most striking correspondences or shifts?
Overview (Top N)
By Phenomenon Type
Statistics 6: Phenomena by Fragment Reference
Which phenomena are adopted in reception testimonies that refer to specific fragments, which are omitted – and which are newly added?
Statistics 7: Phenomena Over Time
How are specific phenomena distributed over time? Bubble size shows in how many reception testimonies of a decade a phenomenon is annotated; colour indicates the phenomenon type.
Overview (Top N)
By Phenomenon Type
Statistics 8: Phenomena by Genre
Which phenomena dominate in which genre? The colour intensity of the cells shows the frequency within each genre; colour indicates the phenomenon type.
Overview (Top N)
By Genre
By Phenomenon Type
Statistics 9: Plot Components
Which phenomena occur together with a particular plot variant? The inner ring shows the phenomenon types, the outer ring the individual phenomena; segment width corresponds to relative frequency.
Statistics 10: References to Persons and Characters
Which persons and person types are, in Sappho fragments as well as in reception testimonies, particularly often not only referenced but also appear as characters? The comparison shows, per person or person type, the frequency of reference and of appearance as a character.
Statistics 11: References to Works and Quotations
Which works are, among the 99 analysed reception testimonies, not only referenced but also quoted?
Statistics 12: Gender-Specific Analyses
What does the gender distribution look like – overall, over time, by genre, and by phenomenon? The gender data comes from Wikidata, is binary, and is mostly not self-identification. For the phenomena, only the authors of the reception testimonies analysed as examples were also taken into account.
Statistics 13: Popularity Analyses Using Wiki Metrics
How popular are authors of German-language Sappho reception testimonies across the wiki universe – and how present are they in the corpus? QRank ranks Wikidata entries by combining page views from Wikipedia, Wikispecies, Wikibooks, Wikiquote, and other Wikimedia projects. Sitelinks are the Wikipedia language versions of articles. Corpus presence indicates how many reception testimonies a person is represented by in the corpus.
QRank vs. Corpus Presence
Each point is an author. The X-axis shows corpus presence (number of reception testimonies in the corpus), the Y-axis shows QRank (wiki popularity).
Top Authors by QRank