MétaCan
ENFR
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.

Export CSVExport JSONAPI query

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
Complex Systems and Decision Making
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.

1,260 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.
1,260 works in the cohort · of 4,299,418page 24 of 26

Labels cover 11 of 1,260 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 1,260 of 1,260 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.

aboutno affunlabeled
Time‐Lagged Scientific Dialogues
Konstantinos I. Stergiou
2005· article· en· Conservation Biology· Decision Sciences
machine prediction:candidate · stsconsensus · none
0
citations
affno abstractunlabeled
The role of organizational slack in climate change adaptation
A. Griffiths, Monika Winn, Martina K. Linnenluecke
2012· article· en· Queensland's institutional digital repository (The University of Queensland)· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Case Study: Applying the CARRA Model
Heather D. Addy, Cheryl Jeffs
2018· article· en· The National Teaching & Learning Forum· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Conquering Complexity with Active Engagement
Donna Francis
2019· article· en· 2019 ASTC Annual Conference (September 21 - 24)· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Chemical engineering: Past; present and future
S.S.E.H. Elnashaie
2014· article· en· Journal of King Saud University - Engineering Sciences· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Impacting Grand Challenges: A 'Both/And' Approach
Natalie Slawinski, Wendy K. Smith, Robin J. Ely, Tobias Hahn, Andrew J. Hoffman, Anita M. McGahan
2020· article· en· Academy of Management Proceedings· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Temporal Work in Strategy Making
Sarah Kaplan, Wanda J. Orlikowski
2012· article· en· Organization Science· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Apprendre à innover dans un monde incertain
Bernard Hubert, Émilie Coudel, Oliver T. Coomes, Christophe‐Toussaint Soulard, Guy Olivier Faure, Hubert Devautour
2012· book-chapter· fr· Éditions Quæ eBooks· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Crisis Management Lessons from Modelling
Thomas Meszaros, Clément Morier
2015· preprint· en· HAL (Le Centre pour la Communication Scientifique Directe)· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Executive Teams
Reda Alhajj, Jon Rokne
2018· book-chapter· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Organizing at and Beyond the Limits
William H. Starbuck, Moshe Farjoun
2007· article· en· SSRN Electronic Journal· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
The Inseparability of Coordination and Space
Karla Sayegh, Samer Faraj
2016· article· en· Academy of Management Proceedings· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
← previousnext →

How this was built: Screen · Findings · About