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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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ERP Systems Implementation and Impact
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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
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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.

347 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.
347 works in the cohort · of 4,299,418page 2 of 7

Labels cover 1 of 347 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 347 of 347 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
IT innovation persistence
Theophanis C. Stratopoulos, Jee‐Hae Lim
2010· article· en· Communications of the ACM· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
37
citations
affunlabeled
Do the ends justify the means?
R. Willis, M. Chiasson
2007· article· en· Information Technology and People· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
32
citations
aboutno affunlabeled
Organizing for post‐implementation ERP
Kevin P. Gallagher, Vickie Coleman Gallagher
2012· article· en· Journal of Enterprise Information Management· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
31
citations
affunlabeled
ERP systems and management accounting
Gary Spraakman, Winnie O’Grady, Davood Askarany, Chris Akroyd
2018· article· en· Journal of Accounting & Organizational Change· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
23
citations
affaboutunlabeled
Unleashing process orientation
Vinod Kumar, Bahar Movahedi, Kayvan Miri Lavassani, Uma Kumar
2010· article· en· Business Process Management Journal· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
21
citations
affunlabeled
Compensation‐based incentives, ERP and delivery performance
Giovani J.C. da Silveira, Brent Snider, Jaydeep Balakrishnan
2013· article· en· International Journal of Operations & Production Management· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
18
citations
aboutno affunlabeled
Social and Cultural Challenges in ERP Implementation
Sapna Poti, Sanghamitra Bhattacharyya, T.J. Kamalanabhan
2011· article· en· International Journal of Information Systems and Social Change· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
14
citations
affunlabeled
Supply chains and the enterprise
Norm Archer
2006· article· en· Journal of Enterprise Information Management· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
12
citations
affunlabeled
The ERP post-implementation stage: a knowledge transfer challenge
Sylvain Goyette, Luc Cassivi, Mathieu Courchesne, Elie Elia
2022· article· en· International journal of information systems and project management· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
12
citations

How this was built: Screen · Findings · About