Bibliographic record
Abstract
We are again very happy to expand the boundaries of scholarship related to academic advising. As you may know, the NACADA Review seeks to amplify new voices, showcase a variety of methodologies, and embrace different epistemologies; all in service of our goal to broaden our understanding of academic advising as a practice and a field of study.The articles that make up this issue are an excellent example of the many lenses that we can use to better “see” academic advising and its effects on students, practitioners, and institutions of higher education around the world.The issue begins with a piece that follows the traditional social science framework for scholarship, Post COVID-19: A Comparative Assessment of Academic Advising by Peters, Burton, and Rich. The authors grounded their work in the NACADA Core Competencies and Developmental Advising theory, creating a survey to measure undergraduate students' experiences with in-person and online advising options. This research uses quantitative methods to deliver information useful for any practitioner or administrator searching for answers on what today's students want from advising.The next piece is a must-read for anyone interested in scholarship through a humanities lens. Provost's article, Cooling Out Vs. Warming Up: History of the Debate explores how the history of community colleges, as well as the language used to define academic advising at community colleges, influences expectations of advisors at those institutions. This article is essential for anyone interested in constructivist epistemologies or who works at a community college (or both). We encourage everyone to share this article with colleagues and delve into this thought-provoking look at the purpose of advising and community colleges.This issue also includes a qualitative study centering on the voices of former doctoral students who completed their PhD in education. In Doctoral Student Perceptions of Faculty Advisors: Four Supportive Behaviors to Promote Doctoral Completion, authors Roy, McGill, and Bloom analyze how advisors help—and in some cases hinder—these students on the path to degree completion. This is a straight-forward, valuable read for anyone who advises doctoral students or who is considering becoming a doctoral student.Finally, the NACADA Review offers an article we believe will become a classic in the field. Culturally Engaged Career Advising: Gordon's Model Expanded builds upon Gordon's “3-I” approach to career advising. Authors Streufert, Smith, and Hebreard add the dimensions Include and Institutional Design to make the model more culturally engaging and to provide a framework for equity within career and academic advising. Make this article the centerpiece of a summer staff meeting or advisor retreat—your team members and your students will benefit from it, no question.
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 | 0.016 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.020 | 0.014 |
| Insufficient payload (model declined to judge) | 0.068 | 0.034 |
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".