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The record is part of the task: matched-record evaluation of text classifiers across maintenance, safety and recall reporting

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Computer Science > Machine Learning

arXiv:2609.16267 (cs)
[Submitted on 14 Sep 2026]

Title:The record is part of the task: matched-record evaluation of text classifiers across maintenance, safety and recall reporting

View a PDF of the paper titled The record is part of the task: matched-record evaluation of text classifiers across maintenance, safety and recall reporting, by Hisham Ihshaish and 3 other authors
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Abstract:Many operational cases are documented more than once, at different workflow stages and for different purposes, yet model evaluations normally select one of these records before model comparison begins. We treat that selection as part of the evaluation and compare matched records of the same cases under fixed labels and splits in three systems: GE Aerospace repair events, NASA ASRS safety reports and NHTSA vehicle recalls. Across the three GE fields, for events whose label comes from parts transactions independently of the narratives, held-out macro-F1 ranged from 0.33 to 0.91. A difference of 0.46 separated the customer report, written before shop work, from the technician report, written after diagnosis but before the transaction that generates the label. That difference is substantially larger than the representation and architecture differences tested on the same events. The public systems showed different patterns: the NHTSA defect summary remained strongest under every model family tested, whereas the ASRS analyst synopsis outperformed the reporter narrative under learned sequence models but not under lexical baselines. Secondary analyses showed that some model comparisons were also record-dependent. Evaluations should be run on the information available at the intended decision point and should report how both the record and the label were produced.
Comments: 37 pages (18-page article, 3 tables, 7 figures, plus a 19-page supplement with tables and figures numbered S1 onward)
Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL)
ACM classes: I.2.7; H.3.3; I.5.4; J.2
Cite as: arXiv:2609.16267 [cs.LG]
  (or arXiv:2609.16267v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2609.16267
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Hisham Ihshaish [view email]
[v1] Mon, 14 Sep 2026 19:29:45 UTC (318 KB)
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