LA TEORIA POLITICA DELL'ANALISI DEI SISTEMI: DAVID EASTON
Bibliographic record
Abstract
Introduzione Nato, il 24 giugno del 1917, e cresciuto in Canada, David Easton ha completato la sua formazione universitaria all'Università di Toronto (B.A. nel 1939, M.A. nel 1943). La sua successiva carriera accademica è legata a tre delle più importanti università americane, Harvard, Chicago e la University of California. Nel 1947 ha conseguito il Ph. D. ad Harvard, dove era teaching fellow dal 1944. Per quasi un quarto di secolo è stato uno dei più eminenti scienziati politici della University of Chigago (1947-1982), dove divenne full professor nel 1955 e fu nominato Andrei MacLeish Distinguished Service Professor nel 1969. Nel 1981 entrò a far parte del Department of Politics and Society della University of California ad Irvine, dove insegna ancora oggi. Non è possibile, nei limiti di quest'articolo, ricapitolare tutte le sue cariche accademiche, partecipazioni a comitati editoriali, o i suoi incarichi come consulente politico. Basti dire che è stato Presidente della American Political Science Association (1968-89), e membro e vicepresidente della American Academy of Arts and Sciences (1985-88). David Easton ha ricevuto tre lauree ad honorem (dalla McMaster University, dal Kalamazoo College e dalla Free University of Berlin).
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".