diff --git a/man/association-score-functions.Rd b/man/association-score-functions.Rd
index ce83ef9..6e1afce 100644
--- a/man/association-score-functions.Rd
+++ b/man/association-score-functions.Rd
@@ -92,10 +92,13 @@
 designed for (Rychlý 2008), and it is why its values do not depend on the
 corpus size and are comparable across corpora.
 
-When ranking or thresholding by logDice, as \code{\link[=collocationAnalysis]{collocationAnalysis()}} does by
-default, it is therefore worth discarding pairs that are not attested more
-often than expected, with \code{dplyr::filter(O > E)}, or requiring a minimum
-\code{pmi} or \code{ll}.
+Since \code{\link[=collocationAnalysis]{collocationAnalysis()}} ranks and thresholds by logDice, it therefore
+drops collocates occurring less often than expected by default. Its
+\code{minObservedExpectedRatio} parameter controls this: raise it to demand a
+stronger contrast, or set it to 0 to see the unfiltered ranking, for instance
+to study repulsion. \code{\link[=collocationScoreQuery]{collocationScoreQuery()}} does not filter, as there the
+pairs to score are given explicitly. For results obtained otherwise,
+\code{dplyr::filter(O > E)} or a minimum \code{pmi} or \code{ll} has the same effect.
 }
 
 \examples{
diff --git a/man/collocationAnalysis-KorAPConnection-method.Rd b/man/collocationAnalysis-KorAPConnection-method.Rd
index 5a0a43d..ac7e7d9 100644
--- a/man/collocationAnalysis-KorAPConnection-method.Rd
+++ b/man/collocationAnalysis-KorAPConnection-method.Rd
@@ -27,6 +27,7 @@
   threshold = 2,
   localStopwords = c(),
   collocateFilterRegex = "^[:alnum:]+-?[:alnum:]*$",
+  minObservedExpectedRatio = 1,
   queryMissingScores = FALSE,
   missingScoreQuantile = 0.05,
   vcLabel = NA_character_,
@@ -74,14 +75,25 @@
 \item{threshold}{minimum value of \code{thresholdScore} function call to apply collocation analysis recursively (only applied when \code{maxRecurse > 0}).
 Note that the default score, \code{logDice}, expresses how salient a pair is
 rather than how surprising, so that a frequent collocate can pass it while
-co-occurring less often than expected. Adding \code{dplyr::filter(O > E)}, or a
-minimum \code{pmi} or \code{ll}, removes those. See the "Salience versus surprise"
-section of \code{\link{association-score-functions}}.}
+co-occurring less often than expected. \code{minObservedExpectedRatio} keeps
+those out. See the "Salience versus surprise" section of
+\code{\link{association-score-functions}}.}
 
 \item{localStopwords}{vector of stopwords that will not be considered as collocates in the current function call, but that will not be passed to recursive calls}
 
 \item{collocateFilterRegex}{allow only collocates matching the regular expression}
 
+\item{minObservedExpectedRatio}{minimum ratio of observed to expected co-occurrence
+frequency a collocate must reach. Defaults to 1, which keeps only collocates
+that occur at least as often as expected by chance, corresponding to a
+non-negative \code{pmi}. Without it, frequent words can end up among the top
+collocates by \code{logDice} although the node does not attract them at all (see
+the "Salience versus surprise" section of
+\code{\link{association-score-functions}}). Raise it to demand a stronger
+contrast, e.g. 2 for collocates occurring at least twice as often as
+expected, or set it to 0 to switch the filter off and obtain the unfiltered
+result of earlier versions, e.g. in order to study repulsion.}
+
 \item{queryMissingScores}{if TRUE, attempt to retrieve corpus-based association scores for vc/collocate combinations that would otherwise be imputed, by re-querying the KorAP backend without applying the collocate frequency threshold}
 
 \item{missingScoreQuantile}{lower quantile (evaluated per association measure over the pooled result set) that anchors the adaptive floor used for imputing missing scores between virtual corpora; a robust spread is subtracted from this anchor so the imputed values stay at or below the weakest observed scores. Imputed cells are marked in the \verb{imputed*} columns; see the section on interpreting multi-VC comparisons below}