Bibliographic record
Abstract
I am grateful to the Vanguard Foundation for generously funding my research on globalization, human development, and gender concerns. This paper, part of that ongoing project, would not have been possible but for Professor Arlie Hochschild’s keen interest and investment of time, energy, and care. She reversed the global chain of caregivers. I also thank Professors Arlie Hochschild and Barrie Thorne as co-directors of the Center for Working Families, for inviting me to affiliate with the center. Their team, consisting of Bonnie Kwan, Chi-Shan Lin, and Janet Oh, have gone out of their way to provide assistance with loving care. I will always cherish the cordial work atmosphere of the center. I also wish to thank Professors Hochschild and Thorne for their comments on an earlier draft of this paper, and Dr. Gayle Goodman for allowing me to see her unpublished Ph.D. thesis. This paper first summarizes the debate between the development enthusiasts and the development skeptics in the fields of human development and development ethics. Development enthusiasts consider development as freedom, but for the skeptics it is a form of coercion. I differentiate two types of freedom: external and internal. Freedom to and freedom from (the
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.054 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 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".