Minette E. DrumwrightThe Role of Communication Messages and Public Relations Strategies In the Higher Education “Public Good ” Debate: A Study of Four Public Research Universities
Bibliographic record
Abstract
To my mother, Vernestine DeJohnette who has supported my dreams and aspirations all of my life and my father, Jesse Wilson Jr., whose counsel and support has aided me during life’s trials and tribulations. To the memory of my brother, Jesse Wilson III; Uncle George DeJohnette; Aunt Alberta Smith, and Aunt Joella Alexander. Acknowledgements I want to thank Dr. Ed Sharpe for serving as a mentor throughout my career in higher education and for advising and counseling me as co-supervisor of my dissertation committee. I want to extend a special thanks to Dr. Bill Lasher for all the guidance he provided as my Ph.D program advisor and for his role as co-supervisor of my dissertation committee. I also want to thank the additional members of my committee, Dr. Norvell Northcutt, Dr. Sharon Jarvis, and Dr. Minette Drumright for their encouragement, advice and help. Other individuals I would like to recognize include Dr. James Hill, Senior Vice President at the University of Texas at Austin, who supported my goal of obtaining a Ph.D by allowing me time away from the office to attend classes. Special appreciation is
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 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".