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
Topic
Software Engineering Techniques and Practices
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.

1,138 results · 1 filter active ·
Results by year
20002025
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.
1,138 works in the cohort · of 4,299,418page 2 of 23

Labels cover 1 of 1,138 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 1,138 of 1,138 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
What Makes Agile Software Development Agile?
Marco Kuhrmann, Paolo Tell, Regina Hebig, Jil Klünder, Jürgen Münch, Oliver Linssen +41 more
2021· article· en· IEEE Transactions on Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
87
citations
afffundunlabeled
Global software development
Filippo Lanubile, Daniela Damian, Heather L. Oppenheimer
2003· article· en· ACM SIGSOFT Software Engineering Notes· Computer Science
machine prediction:candidate · noneconsensus · none
85
citations
venueno affunlabeled
Requirements Prioritization Techniques Comparison
Amjad Hudaib, Raja Masadeh, Mais Haj Qasem, Abdullah Alzaqebah
2018· article· en· Modern Applied Science· Computer Science
machine prediction:candidate · noneconsensus · none
84
citations
afffundunlabeled
Quantitative WinWin
Günther Ruhe, Armin Eberlein, Dietmar Pfahl
2002· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
84
citations
affunlabeled
Extreme Teaming
Amy C. Edmondson, Jean‐François Harvey
2017· book· en· Computer Science
machine prediction:candidate · noneconsensus · none
75
citations
affunlabeled
Human and social factors of software engineering
Michael John, Frank Maurer, Bjørnar Tessem
2005· article· en· ACM SIGSOFT Software Engineering Notes· Computer Science
machine prediction:candidate · stsconsensus · none
67
citations
affno abstractunlabeled
Perceptions of Agile Practices: A Student Survey
Grigori Melnik, Frank Maurer
2002· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
61
citations
affaboutunlabeled
Cultural patterns in software process mishaps
Eve MacGregor, Yvonne Hsieh, Philippe Kruchten
2005· article· en· ACM SIGSOFT Software Engineering Notes· Computer Science
machine prediction:candidate · noneconsensus · none
61
citations
affunlabeled
Understanding the Role of Use Cases in UML
Brian Dobing, Jeffrey Parsons
2000· article· en· Journal of Database Management· Computer Science
machine prediction:candidate · noneconsensus · none
60
citations
affunlabeled
A methodological leg to stand on
Steve Adolph, Wendy A. Hall, Philippe Kruchten
2008· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
58
citations
affunlabeled
On identifying user complaints of iOS apps
Hammad Khalid
2013· article· en· 2013 35th International Conference on Software Engineering (ICSE)· Computer Science
machine prediction:candidate · noneconsensus · none
54
citations

How this was built: Screen · Findings · About