Bibliographic record
Abstract
I first met Dixie Lee Harris as a student in a course in critical thinking I was teaching at the Center for Inquiry Transnational in Amherst, New York.It was an interesting class comprised of students from college undergraduates to octogenarians, with even a classics professor and a Nobel Prize nominee tossed in.Dixie Lee impressed me by her contrast with the more garrulous members ofthe class: She was quiet, attentive, and watchful, but did not speak up much.I saw her at several other Center events, most recently a year ago in Toronto, Canada.There I made so bold as to speak with her, and she gradually opened up about herself.I had just successfully proposed this series, Lived Values, Valued Lives (LVVL), to the Value Inquiry Books Series editors and was looking for titles.Dixie Lee mentioned that she had a manuscript about her travels around the world, and I said I thought it might fit into my new series.I found Dixie Lee Harris's adventures to be fascinating in a variety of ways.She demonstrates that extensive travel is possible without incurring great expense is instructive.Her experiences have been much closer to the lives of the common folk of the countries she has visited than the typical tourist fare.Her encounters are eye opening even to one who has traveled a fair amount.The Lived Values, Valued Lives Series is comprised of biographies, loosely defined, that express and explore how values appear in, and shape, human lives.We intend to provoke readers to engage in reflective exploration of values expressed in the decisions, actions, and thoughts of philosophically reflective individuals.From one another 's narratives we can learn much and come to consider possibilities that might otherwise never occur to us.Dixie Lee Harris does not have formal training as a philosopher.Her comments about values such as honesty, integrity, or happiness do not appear in a theoretical framework.Yet, in her own way, she characterizes the dilemma we all have with ideals: holding to them as a matter of principle versus compromising them as a matter of practicality.She discussed how compromising our values can hurt us, but that hurt is sometimes valuable.LVVL is a series intended for young readers of all ages looking for inspiration not only for course papers but also for their lives.The value of thoughtful reflection, not conversion, is the aim.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.660 | 0.665 |
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".