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
Synthesis lectures on information concepts, retrieval, and services
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.

21 results · 1 filter active ·
Results by year
20142025
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.
21 works in the cohort · of 4,299,418page 1 of 1

Labels cover 0 of 21 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 21 of 21 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.

affno abstractunlabeled
Analysis and Visualization of Citation Networks
Dangzhi Zhao, Andreas Strotmann
2015· book· en· Synthesis lectures on information concepts, retrieval, and services· Computer Science
machine prediction:candidate · bibliometricsconsensus · none
178
citations
affno abstractunlabeled
Analysis and Visualization of Citation Networks
Dangzhi Zhao, Andreas Strotmann
2015· article· en· Synthesis lectures on information concepts, retrieval, and services· Decision Sciences
machine prediction:candidate · bibliometricsconsensus · none
136
citations
affno abstractunlabeled
Measuring User Engagement
Mounia Lalmas, Heather O’Brien, Elad Yom‐Tov
2014· article· en· Synthesis lectures on information concepts, retrieval, and services· Computer Science
machine prediction:candidate · noneconsensus · none
85
citations
affno abstractunlabeled
Visualization of Citation Networks
Dangzhi Zhao, Andreas Strotmann
2015· book-chapter· en· Synthesis lectures on information concepts, retrieval, and services· Computer Science
machine prediction:candidate · bibliometricsconsensus · none
11
citations
affno abstractunlabeled
Foundations of Citation Analysis
Dangzhi Zhao, Andreas Strotmann
2015· book-chapter· en· Synthesis lectures on information concepts, retrieval, and services· Arts and Humanities
machine prediction:candidate · bibliometricsconsensus · none
4
citations
afffundno abstractunlabeled
User Engagement Research and Practice
Heather O'Brien
2025· book· en· Synthesis lectures on information concepts, retrieval, and services· Social Sciences
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Data Needs
Kathleen Gregory, Laura Koesten
2022· book-chapter· en· Synthesis lectures on information concepts, retrieval, and services· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
3
citations
affno abstractunlabeled
Introduction
Heather O’Brien
2025· book-chapter· en· Synthesis lectures on information concepts, retrieval, and services· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
1
citations
affno abstractunlabeled
Discovering Data
Kathleen Gregory, Laura Koesten
2022· book-chapter· en· Synthesis lectures on information concepts, retrieval, and services· Computer Science
machine prediction:candidate · metaresearchconsensus · none
1
citations
affno abstractunlabeled
Introduction
Kathleen Gregory, Laura Koesten
2022· book-chapter· en· Synthesis lectures on information concepts, retrieval, and services· Business, Management and Accounting
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
1
citations
affno abstractunlabeled
Data Discovery: A Human-Centered View
Kathleen Gregory, Laura Koesten
2022· book-chapter· en· Synthesis lectures on information concepts, retrieval, and services· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Conceptual Approaches to User Engagement
Heather L. O'Brien
2025· book-chapter· en· Synthesis lectures on information concepts, retrieval, and services· Psychology
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Approaches to Measuring User Engagement
Heather L. O'Brien
2025· book-chapter· en· Synthesis lectures on information concepts, retrieval, and services· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
What Drives Serendipity Research?
Lori McCay‐Peet, Elaine G. Toms
2018· book-chapter· en· Synthesis lectures on information concepts, retrieval, and services· Decision Sciences
machine prediction:candidate · metaresearch+bibliometricsconsensus · none
0
citations
affno abstractunlabeled
Data Evaluation and Sensemaking
Kathleen Gregory, Laura Koesten
2022· book-chapter· en· Synthesis lectures on information concepts, retrieval, and services· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
User Engagement with Interactive Information Systems
Heather L. O'Brien
2025· book-chapter· en· Synthesis lectures on information concepts, retrieval, and services· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Conclusions and Future Research Directions
Mounia Lalmas, Heather O’Brien, Elad Yom‐Tov
2015· book-chapter· en· Synthesis lectures on information concepts, retrieval, and services· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Conclusion
Heather O’Brien
2025· book-chapter· en· Synthesis lectures on information concepts, retrieval, and services· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Recommendations for Data Discovery, Sensemaking and Reuse
Kathleen Gregory, Laura Koesten
2022· book-chapter· en· Synthesis lectures on information concepts, retrieval, and services· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
0
citations
affno abstractunlabeled
Influences on User Engagement
Heather L. O'Brien
2025· book-chapter· en· Synthesis lectures on information concepts, retrieval, and services· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About