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Record W4233271398 · doi:10.1007/978-1-4419-1005-9_888

Health Economics

2012· book-chapter· en· W4233271398 on OpenAlexaff
Sheina Orbell, Havah Schneider, Sabrina Esbitt, Jeffrey S. Gonzalez, Erica Shreck, Abigail Batchelder, Yori Gidron, Sarah D. Pressman, Emily D. Hooker, Deborah J. Wiebe, Deborah Rinehart, Laura L. Hayman, Luigi Meneghini, Hiroe Kikuchi, Tamer F. Desouky, Lisa M. McAndrew, Pablo A. Mora, Bonnie Bruce, Tana M. Luger, Peter Alle­beck, Olveen Carrasquillo, Alfred L. McAlister, Kristine M. Molina, Stephen Birch, Amiram Gafni, Linda C. Baumann, Alyssa Karel, Howard Sollins, Catharina Hjortsberg, Lee Sanders, Erin N. Marcus, Vincent Tran, Maartje de Wit, Tibor Hajos, Sheah Rarback, Margaret Wallhagen, Siqin Ye, Jonathan Newman, William Whang, Mark Hamer, Timothy W. Smith, Scott DeBerard, Peter A. Shapiro, Yoichi Chida, Valerie Sabol, Annie T. Ginty, Julian F. Thayer, Yoshinobu Kanda, Carrie Brintz, Timothy Whittaker, Jennifer Wessel, Laura Rodriguez‐Murillo, Rany M. Salem, Yutaka Matsuyama, J. Rick Turner, Neil Schneiderman, John Ruiz, Mariana Garza, Lauren Smith, Nicole Overstreet, Jason W. Mitchell, Osvaldo Rodríguez, Jens Gaab, Oliver T. Wolf, Andrea Croom, Hollie B. Pellosmaa, Steven C. Palmer, Kimberly M. Henderson, Susan A. Everson‐Rose, Cari J. Clark, Jonathan Z. Bakdash, Frank A. Drews, Luis I. García, Lynnee Roane, Michael James Coons, Alyssa Parker, Kelly Flannery, Douglas Carroll, Leah Rosenberg, Molly S. Clark, Katherine T. Fortenberry, Kate L. Jansen, Jennifer Heaney, Elliott A. Beaton

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsMcMaster University
Fundersnot available
KeywordsEconomics

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.125
Threshold uncertainty score0.418

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.1250.036

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 head0.124
GPT teacher head0.458
Teacher spread0.334 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

Quick stats

Citations1
Published2012
Admission routes1
Has abstractno

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