MétaCan
Menu
Cohort builder

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.

Search term
Author
Year range
Sort
Language
Type
Field
Venue
International Conference on Software Engineering
Topic
Retraction
Abstract
Evidence source
Study design
Label agreement
Label status

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
venuejournal
aboutaboutness

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

104 results · 1 filter active ·
Results by year
20012020
Publication date
Categories
Machine labels · sparse coverage
Evidence
Language
Type
Citations
An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
104 works in the cohort · of 4,299,418page 1 of 3

Labels cover 0 of 104 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 104 of 104 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

affunlabeled
A framework for managing cloned product variants
Julia Rubin, Marsha Chećhik
2013· article· en· International Conference on Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
58
citations
affunlabeled
Example-driven modeling: model = abstractions + examples
Kacper Bąk, Dina Zayan, Krzysztof Czarnecki, Michał Antkiewicz, Zinovy Diskin, Andrzej Wąsowski +1 more
2013· article· en· International Conference on Software Engineering· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
25
citations
aboutno affunlabeled
From software requirements to architectures
Jaelson Castro, Jeff Kramer
2001· article· en· International Conference on Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
18
citations
affunlabeled
Partial evaluation of model transformations
Ali Razavi, Kostas Kontogiannis
2012· article· en· International Conference on Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
affunlabeled
CodeTimeline: storytelling with versioning data
Adrian Kuhn, Mirko Stocker
2012· article· en· International Conference on Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
11
citations
affunlabeled
Panel: empirical validation: what, why, when, and how
Robert J. Walker, Lionel Briand, David Notkin, Carolyn Seaman, Walter F. Tichy
2003· article· en· International Conference on Software Engineering· Computer Science
machine prediction:candidate · metaresearchconsensus · none
8
citations
affunlabeled
Feature location based on impact analysis
Abhishek Rohatgi, Abdelwahab Hamou‐Lhadj, Juergen Rilling
2007· article· en· International Conference on Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Mining temporal properties of data invariants
Caroline Lemieux
2015· article· en· International Conference on Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Code repurposing as an assessment tool
Joseph Sant
2015· article· en· International Conference on Software Engineering· Computer Science
machine prediction:candidate · metaresearchconsensus · none
5
citations
aboutno affunlabeled
IT and infrastructure's lost dependability
Heli Tervo, Jarmo J. Ahonen
2008· article· en· International Conference on Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Software clustering based on behavioural features
C. R. Patel, Abdelwahab Hamou‐Lhadj, Juergen Rilling
2007· article· en· International Conference on Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Atomic requirements for software architecting
Matthias Galster, Armin Eberlein, Mahmood Moussavi
2007· article· en· International Conference on Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations

How this was built: Screen · Findings · About