Bibliographic record
Abstract
Every book has a history, but because this book originated as a Stanford University dissertation in the 197os, its history is longer than most.To be completely accurate, my interest in the subject even antedated my graduate school experience.From late 1968 to early 1970, I hosted a weekly League of Women Voters television program devoted to public affairs in San Jose and Santa Clara County.Thus, it is not surprising that when I chose a topic of study at Stanford, I gravitated toward an area I had already come to see as fascinating-and that was before the area had become world famous as "Silicon Valley."Another reason for my choice to study San Jose is the nature of the family in which I grew up.My late father, Glen Ingles, was a newspaper editor in various small California towns.Although I would not live in the Santa Clara Valley-Sunnyvale, to be precise-until adulthood, many of the issues with which this book deals, such as the conflict between development andresource preservation, were part of our nightly dinner-table conversation.~Moreover, in the 1950s my late mother, Alberta Ingles, worked for the Coastal Area Protection League at an office in Laguna Beach.This organization tried to prevent the offshore drilling of oil.I learned many things from my parents; one of the most valuable was to care deeply about my native state.For my dissertation, I chose to focus on the Santa Clara Valley during the Great Depression, because I had read enough of John
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".