Bibliographic record
Abstract
The NACADA Review, now in its third year of publication, has come into its own. And we could not be more gratified. We think you will be pleased with the level of scholarship and the range of articles that you will find herein. This issue would not exist were it not for the energy and contributions of Quentin Alexander, whose editorial guidance directed several articles. We wish to thank Dr. Alexander for championing those works and supporting the idea that all voices should be heard.Among the voices ringing out in this current issue are Chimel and Hurs, who start us off with “Practitioner Inquiry to Develop Antiracist Advising Practice: Investigating Issues of Equity and Access.” McGill and Lazarowicz focus our attention on transfer students in “Increasing Access and Enhancing Transition Experiences of Transfer Students in a 2 +2 Program: A Descriptive Case Study.” Hawkes offers up another provocative article: “Advising Racially and Ethnically Diverse Students in Alternative College Admission Programs,” while Kopp provides new perspectives on “Disrupting Racialized Practice in a Post Pandemic Context.” Finally, Yeung moves us to another special section of the choir of voices—graduate students—in “Critical Race Theory within Graduate Education: Connecting Theory with Practice.” That last clause, connecting theory with practice, is one of the defining attributes of the NACADA Review. To reiterate our anthem: If connecting theory with practice rings true to you, please consider submitting your work to the NACADA Review.The choir voices will likely swell as we note two important books that should hit the shelves around the time this issue of the NACADA Review is published. Comprehensive Advisor Training and Development: Practices That Deliver, edited by Karen L. Archambault and Rebecca L. Hapes, will be appearing in its third edition. In the foreword, Jennifer L. Bloom references your voice: “Please always remember that you matter, the work that you do matters, and your voice matters” (Archambault & Hapes, 2022, p. x). We could not agree more, and we congratulate the editors and authors of this volume.Another recent publication of note is Scholarly Inquiry in Academic Advising (Second Edition), edited by Craig M. McGill, Samantha S. Gizerian, and Peter L. Hagen. Scholarly inquiry into academic advising needs a broad range of voices to convey it in its entirety, and nearly all voices are heard in this volume. Social science approaches to scholarly inquiry can be found there, but also scholarly inquiry from the vantage points of the arts, the humanities, and the natural sciences. Should you aspire to engage in scholarly inquiry in academic advising, perhaps with an eye toward one day publishing here in the NACADA Review, we strongly recommend this latter volume. As Wendy G. Troxel puts it, “This edition of Scholarly Inquiry in Academic Advising tackles both the globalization of academic advising and the expansion of applications and relevant considerations of research designs and paradigms” (McGill et al., 2022, p. xiv).
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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 | 0.008 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.018 | 0.025 |
| Insufficient payload (model declined to judge) | 0.049 | 0.033 |
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".