Bibliographic record
Abstract
Craig M. McGill (he/him/his) is an assistant professor for the Department of Special Education, Counseling and Student Affairs at Kansas State University. Prior to completing a 2-year post-doctoral research fellowship at the University of South Dakota, he was an academic advisor at the University of Nebraska-Lincoln and Florida International University. He holds masters degrees in both music theory from the University of Nebraska-Lincoln and academic advising from Kansas State University; he also holds a doctorate from Florida International University in adult education and human resource development. Dr. McGill serves on the editorial boards for the NACADA Journal , Journal of the First-Year Experience & Students in Transition , New Horizons in Adult Education and Human Resource Development , and Journal of Women and Gender in Higher Education . Dr. McGill is a qualitative researcher with an emphasis on identity (personal, professional, and organizational). His research agenda is focused on social justice and the professionalization of academic advising, and he has also published articles within the fields of musical theatre studies and queer studies. He has given almost 60 advising-related presentations at NACADA state, regional, annual, and international conferences. His publication record consists of two coedited books and over 20 peer-reviewed articles.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.286 | 0.209 |
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".