Bibliographic record
Abstract
If as Archilochus’ famous fragment goes ‘The fox knows many things, but the hedgehog knows one big thing’ then Herbert Simon is, at face value, a star example of a fox. Popularized by Isaiah Berlin (1978), the fox-hedgehog distinction has been interpreted (overly simplistically as Berlin acknowledged) in terms of mutually exclusive or ideal types. Hedgehog-type intelligences are motivated by an overarching grand idea or scheme that they then apply to — or through which they filter — everything else. By contrast, fox-type intelligences are highly adaptive and come up with new ideas more suited to a specific situation or context. We are of the view that the supposed hedgehog-fox dichotomy is way too trite and one-dimensional an assessment of Simon. If there were a golden thread to Simon’s work it would be the development of a more adequate theory of human problem-solving and derivatively (but no less deeply) his interest in the computer simulation of human cognition — all in the service of the former (Frantz and Marsh, 2014). The upshot is that Simon made significant contributions to economics, political science, epistemology, sociology, cognitive science, philosophy, public administration, organization theory, and complexity studies (and more besides); and while ascriptions of ‘polymath’ and ‘Renaissance man’ are not without merit, they gloss over the distinctive quality of such a mind. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".