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Contributors

2017· book-chapter· en· W4243553680 on OpenAlexaff
Suhny Abbara, David Aguilar, Eric H. Awtry, José L. Báez-Escudero, Faisal G. Bakaeen, Gary Balady, Luc Beauchesne, Sheilah Bernard, Ozlem Bilen, Itamar Birnbaum, Yochai Birnbaum, Fernando Boccalandro, Biykem Bozkurt, Blasé A. Carabello, Jaya Chandrasekhar, Leslie T. Cooper, Luke Cunningham, Ali E. Denktas, Anita Deswal, Haytham Elgharably, Lothar Faber, Michael E. Farkouh, Nadeen Faza, Savitri Fedson, G. Michael Felker, James J. Fenton, Michael E. Field, Scott D. Flamm, Lee A. Fleisher, Laura Epstein Flink, Amy J. French, Marat Fudim, Stephen Gannon, Nicholas Governatori, Cindy L. Grines, Gabriel B. Habib, Ihab Hamzeh, Tomoya Hinohara, Vu Hoang, Brian D. Hoit, Hani Jneid, José A. Joglar, Douglas R. Johnston, Lee Joseph, Waleed Kayani, Thomas A. Kent, Jimmy Kerrigan, Elias Kfoury, Shaden Khalaf, Mirza Umair Khalid, Esther S.H. Kim, Panos Kougias, Amar Krishnaswamy, Michael H. Kroll, Nitin Kulkarni, Richard A. Lange, Salvatore Mangione, Sharyl Martini, James McCord, Roxana Mehran, Geno J. Merli, Stephanie L. Mick, Curtiss Moore, Ajith Nair, Vijay Nambi, Heidi Nicewarner, E. Magnus Ohman, Nicolas L. Palaskas, Lavannya M. Pandit, Niraj R. Patel, Lawrence S. Phillips, Andrew Pipe, Charles V. Pollack, Mark Pollet, Stuart B. Prenner, Prabhakar Rajiah, Moisés Rodríguez‐Mañero, Eric E. Roselli, Zeenat Safdar, Catalina Sánchez-Álvarez, Paul Schürmann, Nishant R. Shah, Sanjiv J. Shah, Tina Shah, Fidaa Shaib, Mandeep S. Sidhu, Edward G. Soltesz, Sarah A. Spinler, Ya-min Sun, Luis Tamara, Victor F. Tapson, Alisa Thamwiwat, Paaladinesh Thavendiranathan, Rahul Thomas, Kara Thompson, Megan Titas, Michael Z. Tong, Miguel Valderrábano, Andrew M. Vekstein, Salim S. Virani, Fawad Virk, Hercilia Von Schoettler, Aaron S. Weinberg, Ahmad Zeeshan

Bibliographic record

VenueElsevier eBooks · 2017
Typebook-chapter
Languageen
Field
Topic
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBusinessComputer science

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.329
Threshold uncertainty score0.470

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0020.001
Scholarly communication0.0070.005
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.6710.601

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.262
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2017
Admission routes1
Has abstractno

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