MétaCan
Menu
Back to cohort
Record W4254005735 · doi:10.1111/fire.12238

Issue Information

2021· paratext· en· W4254005735 on OpenAlexfundno aff
Thomas W. Doellman, Jennifer Itzkowitz, Jesse Itzkowitz, Sabuhi Sardarli, Serkan Karadas, Louis Louis, Gagnon Goktan, Robert L. Kieschnick, Rabih Moussawi, Bradley Benson, Rama Iyer, Kristopher J. Kemper, Jing Zhao, James Upson, Johnson Hardy, Dan Delisle, Maria French, Paul Schutte, Musa Brockman, Cihan Subasi, Palani-Rajan, Alex Kadapakkam, John Meisami, Viktor Wald, Michael A. Goldstein, Kenneth R. French, George Andrew Karolyi, Michelle Lowry, Paul Schultz, Matthew Spiegel

Bibliographic record

VenueFinancial Review · 2021
Typeparatext
Languageen
FieldEngineering
TopicHuman auditory perception and evaluation
Canadian institutionsnot available
FundersUniversität ZürichUniversity of TorontoQueen's UniversityUniversity of Technology SydneyUniversity of New South WalesUniversity of CambridgeBentley UniversityHebrew University of JerusalemRice UniversityTemple UniversityUniversity of Central FloridaUniversity of Texas at San AntonioDrexel UniversityUniversity of Notre DameUniversity of MinnesotaShanghai Jiao Tong UniversityVillanova UniversityUniversity of CincinnatiSyracuse UniversityGeorgia Institute of Technology
KeywordsCitationComputer scienceInformation retrievalWorld Wide WebInternet privacy

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)0.003
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.832
Threshold uncertainty score0.240

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.004
Science and technology studies0.0010.001
Scholarly communication0.0080.002
Open science0.0020.002
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.8320.768

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 head0.021
GPT teacher head0.289
Teacher spread0.268 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractno

Explore more

Same venueFinancial ReviewSame topicHuman auditory perception and evaluationFrench-language works237,207