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Contributors

2025· book-chapter· en· W4415018083 on OpenAlexaff
Samantha Vieira Abbad, Reham I. Abdelhamid, Atman Adiba, Arjun Kumar Aggrawal, Niaz Ahmed, Arumugam Vijaya Anand, Fabrício Barbosa Monteiro Arraes, Vadivelu Bharathi, Sourav Bhattacharjee, Abdellatif Boutagayout, Ankan Das, Sheetal Das, Sandip Debnath, Marcelo Picanço de Farias, Thounaojam Premlata Devi, Tuward J. Dweh, Sozan E. El-Abeid, Hala.G.A.G. Elfiky, Mohamed Elshafiey, Rachid Ezzouggari, Abdelaaziz Farhaoui, Muhammad Farooq, Katia Cristiane Brumatti Gonçalves, Richa Gupta, K. Jayachandran, Indrajit Kalita, Sharmistha Sarma Kalita, Srinivasan Kameswaran, Mohamed Kouighat, Salah‐Eddine Laasli, Rachid Lahlali, Yengkhom Linthoingambi Devi, Manaswini Mahapatra, Sibasis Mahapatra, Soumya Ranjan Mahapatra, Ramasamy Manikandan, Mani Manoj, Sumit Kumar Mishra, Suchismita Mishra, Mayla Daiane Corrêa Molinari, Hugo Bruno Correa Molinari, K. Nimitha, Renata Fuganti‐Pagliarini, Carolina Almeida Brito Picanço, Biswajit Pramanik, Sourish Pramanik, E.K. Radhakrishnan, Selvaraju Ragavi, Mandala Ramakrishna, Ghulam Raza, Muhammad Khuram Razzaq, Asirvatham Alwin Robert, Lorena Maria Rudnik, Manalisha Saharia, Jyoti Prakash Sahoo, Salman Saleem, Richa Saxena, Dibyendu Seth, Muhammad Shafiq, Richa Sharma, Aditya Pratap Singh, Siddhartha Singh, Dhanawantari L. Singha, Ahmed G. Solaiman, Vaishnavi Srivastava, B. Swapna, Bhaben Tanti, Simile Tripathy, V. S. Veena, Angelina Thomas Villikudathil, Verinder Virk, Kai Wang

Bibliographic record

VenueElsevier eBooks · 2025
Typebook-chapter
Languageen
Field
Topic
Canadian institutionsSemtech (Canada)
Fundersnot available
Keywordsnot available

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 score0.258
Threshold uncertainty score0.369

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)0.7420.676

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 head0.012
GPT teacher head0.245
Teacher spread0.233 · 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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Citations0
Published2025
Admission routes1
Has abstractno

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