On the Contribution of David J. M. Hooson to the Geographical Study of the Soviet Union
Bibliographic record
Abstract
Abstract Notes 1. David J. M. Hooson, ‘The Distribution of Population as the Essential Geographical Expression’, The Canadian Geographer 17 (1960) pp. 10–20. 2. Ibid., pp. 16–17. 3. David J. M. Hooson, A New Soviet Heartland? (Princeton, NJ: Van Norstrand Co. 1964) p. 12. 4. David J. M. Hooson, ‘The Middle-Volga – An Emerging Focal Region in the Soviet Union’, The Geographical Journal 126 (1960) pp. 180–190. 5. Ibid., p. 180. 6. Ibid., p. 185. 7. David J. M. Hooson, ‘The Soviet Union and the Geography Student’, The Canadian Geographer 6 (1962) pp. 78–82. 8. Ibid., p. 82. 9. David J. M. Hooson, ‘A New Soviet Heartland?’, The Geographical Journal 128 (1962) p. 19. 10. Hooson, A New Soviet Heartland? (note 3) p. 121. 11. Ibid., p. 123. 12. Ibid., p. 126. 13. David J. M. Hooson, The Soviet Union – A Systematic Regional Geography (London: University of London Press 1966). 14. Roy E. H. Mellor, ‘Review of Dewdney, J. C.’, Geography of the Soviet Union, Geography, 53 (1968) pp. 440. 15. Hooson, The Soviet Union (note 13) pp. 342–343. 16. Ibid., p. 346. 17. David J. M. Hooson, ‘The Outlook for Regional Development in the Soviet Union’, Slavic Review 31 (1972) pp. 536–554 and 571–573. 18. Ibid., p. 553. 19. Personal Communication with Robert North, 10 July 2008. 20. Personal Communication with James Bater, 17 July 2008. 21. Personal Communication with Leslie Dienes, 10 July 2008. 22. Personal Communication with Craig ZumBrunnen, 18 July 2008. 23. The findings were published in: Michael Bradshaw and Jessica Pendergrast, ‘The Russian Heartland Revisited: An Assessment of Russia's Transformation’, Eurasian Geography and Economics 46 (2005) pp. 83–122. Further information on the project and its outputs can be found at: .
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".