First-Generation College Student Dissertation Abstracts: Research Strategies, Topical Analysis, and Lessons Learned
Bibliographic record
Abstract
First-generation college students are students whose parents or guardians did not obtain a four year collegedegree (Davis, 2012). As a group these students make up a large part of the college student population and areoften reported to encounter difficulties in their campus experience. While the topic of first-generation student hasreceived much attention over the past years, no research effort has been reported that examines dissertations onthe topic. This article utilizes a bounded qualitative synthesis study framework to examine the 133 dissertationabstracts found by searching the ProQuest Dissertation and Theses TM digital database for dissertations abstractsfrom 2009 through 2013 using the search terms “first-generation college students” and “higher education.” Theresearch question for this study was: What can we learn from the examination of doctoral dissertation abstractsthat focus on the experience of first-generation college students regarding research strategies, topics addressed,and lessons learned? The study’s findings provide an overview of researcher attributes and the characteristics ofthe research in terms of methodology and topical focus. “Lessons learned” from the abstracts as well as theomissions in the research are presented. A major finding of the investigation was that very few of thedissertations have entered the academic conversation regarding first generation students – major books on thetopic do not reference the dissertations and in a search of academic journals only three of the 133 dissertationswere found to have been published.
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 | 0.116 | 0.218 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".