Bibliographic record
Abstract
This introduction presents an overview of the key concepts discussed in the subsequent chapters of this book. The book discusses the Goffman’s sociological ideas have contributed to the shaping of fields of study, such as media studies, digital studies, mobility studies, disability studies, death studies, police studies, and so on. The post allowed Goffman to devote a substantial portion of his time – some 12 months between December 1949 and May 1951 – to conduct fieldwork in a rural community on the remote Shetland island of Unst. The publication of the Edinburgh edition of The Presentation of Self in Everyday Life launched Goffman’s distinctive way of doing sociology. Born on 11 June 1922 in Mannville, Alberta, Erving Goffman was the second child of Max and Anne Goffman, Jewish immigrants from Ukraine. Goffman then began graduate work at the University of Chicago. A massive influx of ex-servicemen used the US government’s GI Bill to finance their studies.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.815 | 0.003 |
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".