Bibliographic record
Abstract
Bang, Haeun (Grace), University of North Carolina at Greensboro, NC Breitbach, Elizabeth, Darla Moore School of Business – University of South Carolina, Columbia, SC Burr, Brandon, Utah Valley University, Orem, UT Ceballos, Lina, Universidad EAFIT, Medellin, Colombia Chaney, Cassandra, Louisiana State University, Baton Rouge, LA Childs, Michelle, University of Tennessee, Knoxville, Knoxville, TN Cho, Soo Hyun, California State University, Long Beach, Long Beach, CA Choi, Juwon, North Dakota State University, Fargo, ND Das, Debanjan, West Virginia University, Morgantown, WV Earhart, Carla, Ball State University, Muncie, IN Fan, Lu, University of Georgia, Athens, GA Fisher, Patti, Virginia Tech, Blacksburg, VA Fu, Wei, Maryville College, Maryville, TN Goff, Emily, North Dakota State University, Fargo, ND Graves, Nicole, South Dakota State University, Brookings, SD Griesdorn, Tim, University of the Incarnate Word, San Antonio, TX Hancock, Natalie, Brigham Young University, Provo, UT Handy, Deborah, Washington State University, Pullman, WA Harden, Amy, Ball State University, Muncie, IN Herring, Angel, University of Southern MS, Hattiesburg, MS Huang, Shuyue, Mount Saint Vincent University, Halifax, Nova Scotia, Canada Huang, Yu Chih, Clemson University, Taiwan Hubler, Daniel, Weber State University, Ogden, UT Jai, Tun-Min, TX Tech University, Lubbock, TX Johnson, Olivia, University of Houston, Houston, TX Jones, Katie, West Virginia University, Morgantown, WV Kang, Ju-Young, University of Hawai'i, Honolulu, HI Kim, KyoungTae, University of Alabama, Tuscaloosa, AL Korankye, Thomas, University of Arizona, Tucson, AZ Lee, Jae Min, Minnesota State University, Moorhead, MN Lee, Jaeha, North Dakota State University, Fargo, ND Lee, Sunwoo, York University, Toronto, Ontario, Canada Lee, Yuri, Seoul National University, Seoul, Korea (the Republic of) Legendre, Tiffany, University of Houston, Houston, TX Levitt, Jamie, California State University, Fresno, Fresno, CA Lima, Joana, University of Evora, Evora, Portugal Lin, Shu-Hwa, University of Hawai'i, Honolulu, HI Ma, Weiyi, University of Arkansas, Fayetteville, AR Matthews, Delisia, NC State Univeristy, Raleigh, NC Ouyang, Congrong, Kansas State University, Manhattan, KS Park, Kwangsoo, Purdue University Northwest, Hammond, IN Park, Narang, University of Georgia, Athens, GA Park, Phillip, University of North Texas, Denton, TX Park, Seunghyun, St. John's University, Queens, NY Parsons, Jean, University of Missouri, Columbia, MO Pendergast, Donna, Griffith University, Nathan, Queensland, Australia Ray-Degges, Susan, North Dakota State University, Fargo, ND Rea, Jennifer, University of Minnesota, Moorhead, MN Rea, Jenny, University of Arizona, Tucson, AZ Rowley, Micheal, Illinois State University, Normal, IL Russell, Luke, Illinois State University, Normal, IL Sadachar, Amrut, Auburn University, Auburn, AL Scott, Brigitte, Virginia Tech, Blacksburg, VA Shen, Dong, Sacramento State University, Sacramento, CA Shephard, Arlesa, Buffalo State University, Buffalo, NY Skobba, Kimberly, University of Georgia, Athens, GA Song, Seobgyu, Kyungpook National University, Sangju, Korea (the Republic of) Stebbins, Richard, University of Alabama, Tuscaloosa, AL Terrell, Amanda, University of Arkansas, Fayetteville, AR Tuliao, Minerva D., Texas Tech University, Lubbock, TX Turgeson, Susan, University of Wisconsin – Stevens Point, Stevens Point, WI Vollmer, Rachel, Bradley University, Peoria, IL Wilmarth, Melissa, University of Alabama, Tuscaloosa, AL
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.016 | 0.008 |
| Insufficient payload (model declined to judge) | 0.440 | 0.402 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".