Navigating White Waters: Generation Z Untraditional College Transition Amid Unprecedented Social, Health, and Academic Crisis
Bibliographic record
Abstract
Over the last three years, crises of a historical magnitude have had a profound impact on the higher education system in the U.S. During the spring of March 2020, COVID-19, referred to as the coronavirus, caused a significant health crisis, killing hundreds of thousands of people, while disrupting the educational, economic, and health system (Gupta, 2021). The following year, a 46-year-old black man, George Floyd, was brutally murdered by a white police officer, sparking violent protests and debate around racial equity, policing, and justice. A toxic and polarizing political environment further complicated issues under the controversial leadership of President Donald Trump. Colleges and universities had to quickly pivot to remote instruction, enforce mask mandates, and carefully navigate discourse to minimize disruption to the education of students. The adjustment was challenging for most institutions, particularly those classified as Hispanic Serving Institutions (HSI) or Minority Serving Institutions (MSI). They are usually under-resourced but serve many marginalized, low-income, first-generation, and at-risk students. These organizations encountered both obstacles and opportunities in the attempt to usher in a new generation of learners, Generation Z. Generation Z, often referred to as Gen-Z, are those individuals born between the years 1995-2015; a group that has experienced an untraditional and unprecedented college transition that will have a lasting impact on their younger and older adult lives. This qualitative case study explored the lived experience of Generation Z college students as they navigated the uncertain and tumultuous college transition into an HSI/MSI during the large-scale U.S. and world crises.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".