Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T04:18:04.047093Z
Paper Citation Record · LEDGER
As of 23 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2508.03881.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T04:18:04.047093Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
48 of 48 outbound references displayed
External citation measurements
No source-named external measurement is stored.
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Peeking inside the black-box: a survey on explainable artificial intelligence (xai),
Reference 1
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Revealing the complexity of automotive software,
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Understanding large-scale software systems – structure and flows,
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Explainability as a non-functional requirement,
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Exploring explainability: a definition, a model, and a knowledge catalogue,
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Exploring the means to measure explainability: Metrics, heuristics and questionnaires,
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential How explainable is your system? towards a quality model for ex- plainability,
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Iden- tifying explanation needs: Towards a catalog of user-based indicators,
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Accu3rate: A mobile health application rating scale based on user reviews,
Reference 9
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Privacy explanations–a means to end-user trust,
Reference 10
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Quo vadis, ex- plainability? – a research roadmap for explainability engineering,
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential A live extensible ontology of quality factors for textual requirements,
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential How can we develop explainable systems? insights from a literature review and an interview study,
Reference 13
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential How does users’ app knowledge influence the preferred level of detail and format of software explanations?
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential A study on the men- tal models of users concerning existing software,
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential What can be concluded from user feedback? - an empirical study,
Reference 16
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential How to elicit explainability requirements? a com- parison of interviews, focus groups, and surveys,
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Explanation needs in app reviews: Taxonomy and automated detection,
Reference 18
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Automating explanation need management in app reviews: A case study from the navigation app industry,
Reference 19
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Designing end-user personas for explainability requirements using mixed methods research,
Reference 20
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Modeling and evaluating per- sonas with software explainability requirements,
Reference 21
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Revealing the role of user moods in struggling search tasks,
Reference 22
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Explanations in everyday software systems: Towards a taxonomy for explainability needs,
Reference 23
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Framing what can be explained – an operational taxonomy for explainability needs,
Reference 24
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Prado: Predicting app adoption by learning the correlation between developer-controllable properties and user behaviors,
Reference 25
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Dataset: Gold standard dataset for explainability need detection in app reviews
Reference 26
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Towards public understanding of software through mod- eling,
Reference 27
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Observation dff35642-b1d8-4c38-944e-15176b5933b6 · outbound
From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Context, content, consent- how to design user-centered privacy explanations (s)
Reference 28
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Explanations on demand-a technique for eliciting the actual need for explanations,
Reference 29
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential The x factor: On the relationship between user experi- ence and explainability,
Reference 30
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential A systematic review and taxonomy of expla- nations in decision support and recommender systems,
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Explainability as a non-functional requirement: challenges and recommendations,
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Do users’ explainability needs in software change with mood?
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Source-reported events for the cited work
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Cases for explainable software systems: Characteristics and examples,
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Automatic generation of explainability requirements and software explanations from user reviews,
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Investigating the criticality of user-reported issues through their relations with app rating,
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential The app sampling problem for app store mining,
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Source-reported events for the cited work
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Evaluating pre-trained models for user feedback analysis in software engineering: a study on classification of app-reviews,
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential A coefficient of agreement for nominal scales,
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Source-reported events for the cited work
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential The measurement of observer agreement for categorical data
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Bonferroni correction,
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Cram ´er, Mathematical Methods of Statistics
Reference 42
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Das fehlergesetz und seine verallgemeinerungen durch fechner und pearson: A rejoinder,
Reference 43
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential The proof and measurement of association between two things,
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Note on regression and inheritance in the case of two parents,
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Cohen, Statistical Power Analysis for the Behavioral Sciences , 2nd ed
Reference 46
Source-reported events for the cited work
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Dataset: From app features to explanation needs: Analyzing correlations and predictive potential,
Reference 47
Source-reported events for the cited work
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From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Wohlin, P
Reference 48
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
No inbound Pith citation observations are available.