Bibliographic record
Abstract
Abstract In the turbulent 1970s, Amo Houghton began thinking long and hard about the problem of succession. Although he was then in his early fifties, he had been a senior officer of the company for a quarter century and had served as chairman and CEO since 1964. The problem now was not that he lacked energy or enthusiasm. Rather, he recognized the need to create opportunities in the company for younger executives. In a series of conversations that Tom MacAvoy initiated, the two men reflected on how they had risen to the top at relatively young ages and remarked on the need to provide their successors with time to make an impact. Amo was also intrigued by possibilities of finding new contexts in which to apply his talents. Of course, there was a family tradition of leaving early. His grandfather, Alanson B. Houghton, had embarked on his political and diplomatic career while in his mid-fifties. Arno’s father, the ambassador, had begun his second career in public service at an even earlier age during World War II, then returned to the company, and left management for good at age 58.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".