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4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

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Software Engineering Research
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Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

affaffiliation
fundfunder
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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

3,468 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
3,468 works in the cohort · of 4,299,418page 21 of 70

Labels cover 10 of 3,468 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 3,468 of 3,468 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

affunlabeled
Managing technical debt in software development
İpek Özkaya, Philippe Kruchten, Robert L. Nord, Nanette Brown
2011· article· en· ACM SIGSOFT Software Engineering Notes· Computer Science
distilled prediction:candidate · metaresearch+metaepi_narrowconsensus · none
24
citations
affunlabeled
On Automatic Detection of Performance Bugs
Sokratis Tsakiltsidis, Andriy Miranskyy, Elie Mazzawi
2016· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
24
citations
affunlabeled
Pragmatic software reuse
Reid Holmes
2008· article· fr· PRISM (University of Calgary)· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
24
citations
affunlabeled
PLIERS
Michael Coblenz, Gauri Kambhatla, Paulette Koronkevich, Jenna Wise, Celeste Barnaby, Joshua Sunshine +2 more
2021· article· en· ACM Transactions on Computer-Human Interaction· Computer Science
distilled prediction:candidate · noneconsensus · none
24
citations
afffundno abstractunlabeled
Predicting defective modules in different test phases
Bora Çağlayan, Ayşe Tosun, Ayşe Bener, Andriy Miranskyy
2014· article· en· Software Quality Journal· Computer Science
distilled prediction:candidate · metaresearchconsensus · none
24
citations
affunlabeled
Code review practices for refactoring changes
Eman Abdullah AlOmar, Moataz Chouchen, Mohamed Wiem Mkaouer, Ali Ouni
2022· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
24
citations
affno abstractunlabeled
Patterns of continuous requirements clarification
Eric Knauss, Daniela Damian, Jane Cleland‐Huang, Remko Helms
2014· article· en· Requirements Engineering· Computer Science
distilled prediction:candidate · noneconsensus · none
23
citations
affunlabeled
PYREF: Refactoring Detection in Python Projects
Hassan Atwi, Bin Lin, Nikolaos Tsantalis, Yutaro Kashiwa, Yasutaka Kamei, Naoyasu Ubayashi +2 more
2021· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
23
citations
affunlabeled
Recovering binary class relationships
Yann‐Gaël Guéhéneuc, Hervé Albin-Amiot
2004· article· en· ACM SIGPLAN Notices· Computer Science
distilled prediction:candidate · noneconsensus · none
23
citations
afffundunlabeled
Refactoring to aspects
Jan Hannemann, Thomas Fritz, Gail C. Murphy
2003· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
23
citations
affunlabeled
ApacheJIT
Hossein Keshavarz, Meiyappan Nagappan
2022· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
22
citations

How this was built: Screen · Findings · About