Bibliographic record
Abstract
As part of the National Collegiate Honors Council’s (2022) collection of essays about the value of honors to its graduates (1967–2019), the author reflects on the personal and professional impacts of the honors experience. I am a Southern Appalachian, first-generation college student from a small town—a place where folks are sometimes considered backwards, ignorant, and or even a bit “simple minded.” Coming to Emory & Henry College, I was certainly among the lesserprepared students in my honors cohort. I did not attend a Governor’s School, I did not have lessons with local college professors, and I did not meet the test-score requirements for the program. And while I was “in,” there was an unspoken doubt. Immediately, my education was questioned in its rigor and breadth. I know this doubt reflected the high academic standards of the program, but it also carried unspoken assumptions about the place I came from. I would spend the next four years working to dismantle this doubt, proving myself capable and succeeding where I felt I was expected to fail. Ultimately, the Honors Program facilitated this success.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.007 |
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".