MétaCan
Menu
← Back to cohort

Contributors

2023· book-chapter· en· W4381955597 on OpenAlexaff
Vibhav Agrawal, Abdulla Al‐Ansari, Khalid Al‐Rumaihi, Mohamed Arafa, Elizabeth Austin, Babatunde Abiodun Balogun, Anirban Bhowmik, Hannah Bradwell, Zahid A Butt, Bappaditya Chowdhury, Robyn Clay‐Williams, Leonie Cooper, Michael F. Cullinan, Gareth Davies, Avinash De Sousa, Joydeep Dey, Dhanya Manayath, Daniele Doneddu, Walid El Ansari, Haytham Elmiligi, Sk. Samim Ferdows, Florian Fischer, Fayez Gebali, Magali Goirand, Lewis Hassell, Seyed Mehdi Hazrati Fard, Thomas E. Howson, T. James, Shubhangi Jangle, Ajeya Jha, Lovleen Tina Joshi, Naomi Joyce, Sunil Karforma, Jitendra Kumar, Joe Linogao, Pragya Lodha, Mohammad Mamun, Conor McGinn, Arka Mitra, Marco Moreno-Ibarra, Samrat Kumar Mukherjee, Öznur Özaltın, Carolina Palma-Preciado, Liron Pantanowitz, Fadi Qasem, Timothy Ramseyer, Marie-Christin Redlich, Daniel Rees, Abdiel Reyes-Vera, Kim L. Roberts, Magdalena Saldaña-Pérez, Akila Sarirete, Abhijit Sarkar, Michael Schaller, Robert Scott, Grigori Sidorov, Ankit Singh, Abdülhamit Subaşı, Muhammed Enes Subasi, Joanne Taylor, P. K. Viswanathan, Özgür Yeniay, Rajiv Yeravdekar

Bibliographic record

VenueElsevier eBooks · 2023
Typebook-chapter
Languageen
Field
Topic
Canadian institutionsUniversity of WaterlooOntario Tech UniversityUniversity of Victoria
Fundersnot available
KeywordsComputer 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)0.000
metaresearch head score (Gemma)0.002
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 score0.262
Threshold uncertainty score0.373

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0020.001
Scholarly communication0.0060.004
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.7380.674

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 head0.024
GPT teacher head0.253
Teacher spread0.229 · 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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractno

Explore more

Same venueElsevier eBooks→French-language works237,207→