Das social Kapital : institutions and entrepreneurial networks in Russia's exit from socialism
Bibliographic record
Abstract
innovation spans decades.These interests were likely sources of his inquisitiveness in the issues I was grappling with.My early views on the challenges facing Russia's first generation entrepreneurs and their tendency to act independently and avoid rather than engage government in a dialogue all spoke to Röling's interest in the potential of platforms and social learning to facilitate collective action.By the end of his visit, he had offered in principle to be my PhD supervisor, should I be interested.While Niels was a major influence behind this book, he is not alone.I owe a special gratitude to Professor Asit Sarkar who was the 'father' of the YDFP program.Asit hired me to direct the YDFP in its final years and to work with him to internationalize the research and learning environment at the University of Saskatchewan.Asit's interests trespass disciplines and geography, and his commitment to research without borders was an inspiration to me.In researching and writing this book, I also benefited from numerous collaborators in Canada and in Russia.As the penultimate director of the YDF program, I was responsible for organizing a ten year program evaluation (University of Saskatchewan & Universalia 2003).This evaluation was informed by three separate initiatives.First, an association of YDF Fellows called the Russian-Canadian Club of President's Fellows coordinated the Profiles in Transition project (Mikheev, O'Brien, et al. 2003).The objective of this book project was to give space to YDF Fellows to reflect on how the program impacted their personal and professional lives.Many YDF Fellows contributed articles and opinion pieces to this project.Second, a public sector conference and three private sector workshops brought together YDF Fellows and partner organizations in structured forums to assess the design and the impact of the program.Finally, the Canadian International Development Agency sponsored a focused research project.Together with Professors Li Zong and Harley Dickinson, from the Department of Sociology at the University of Saskatchewan, and Drs.Marina Larionova and Galina Gradoselskaya from the Federal Commission Secretariat, we examined the emergence and contribution of graduate networks to a range of program goals.A considerable portion of the empirical data and analysis from the two reports and an article emerging from this research project are incorporated into this book (O'Brien, Zong, et al. 2002a, 2002b; O'Brien, Zong, et al. 2003).I would like to thank these collaborators and the Canadian International Development Agency for supporting this work.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".