Bibliographic record
Abstract
Tarshis was born in Toronto, Canada on 22 March 1911. After a commerce degree at the University of Toronto, he went to Trinity College, Cambridge, where he took a BA in 1934 and a PhD in 1939. His years in Cambridge, 1932–6, which coincided with the emergence of Keynes’s General Theory, shaped much of his subsequent professional life. His notes for Keynes’s annual series of eight lectures on his work in progress for the years 1932–5 have become an important source for those interested in tracing the evolution of Keynes’s views. The two Cambridge revolutions of the 1930s, Keynes’s and imperfect competition, focused the analysis of his PhD dissertation, ‘The Distribution of Labour Income’. From this came two classic articles in 1938 and 1939 which, along with a contemporaneous piece by John Dunlop (1938), forced Keynes to reconsider his generalization that real and money wages moved inversely over the trade cycle and its implications for the assumption of perfect competition that underlay the analysis of the book (Keynes 1939).
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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 teacher head, 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".