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).
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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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; both teacher heads agree on what is shown here.
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".