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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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Service and Product Innovation
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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.

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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.

402 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.
402 works in the cohort · of 4,299,418page 1 of 9

Labels cover 3 of 402 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 402 of 402 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
Putting Consumers to Work
Detlev Zwick, Samuel K. Bonsu, Aron Darmody
2008· article· en· Journal of Consumer Culture· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
692
citations
affunlabeled
The Service Delivery Network (SDN)
Stephen S. Tax, David McCutcheon, Ian Wilkinson
2013· article· en· Journal of Service Research· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
312
citations
affunlabeled
Service operations: what’s next?
Joy M. Field, Liana Victorino, Ryan W. Buell, Michael J. Dixon, Susan Meyer Goldstein, Larry J. Menor +4 more
2018· article· en· Journal of service management· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
99
citations
affunlabeled
Understanding Telehealth
Joachim Jean-Jules, Alain Villeneuve
2011· book-chapter· en· IGI Global eBooks· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
44
citations
affunlabeled
Professional service supply chains⋆
Jean Harvey
2016· article· en· Journal of Operations Management· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
41
citations
affunlabeled
From service quality to service theory and practice
Chatura Ranaweera, Μαριάννα Σιγάλα
2015· article· en· Journal of Service Theory and Practice· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
39
citations
affunlabeled
Accelerating employee-related scholarship in service management
Mahesh Subramony, Karen Holcombe Ehrhart, Markus Groth, Brooks C. Holtom, Danielle D. van Jaarsveld, Dana Yagil +6 more
2017· article· en· Journal of service management· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
38
citations
affunlabeled
A framework for sustainable service system configuration
Allard C.R. van Riel, Jie J. Zhang, Lee Phillip McGinnis, Mohammad G. Nejad, Milos Bujisic, Paul Phillips
2019· article· en· Journal of service management· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
37
citations
affunlabeled
Service operations: what have we learned?
Liana Victorino, Joy M. Field, Ryan W. Buell, Michael J. Dixon, Susan Meyer Goldstein, Larry J. Menor +4 more
2018· article· en· Journal of service management· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
36
citations

How this was built: Screen · Findings · About