Shouldn’t, Wouldn’t, Couldn’t? Analyzing the Involvement of Oligarchs’ Philanthropy Foundations in the Ukrainian Protests of 2013-14
Bibliographic record
Abstract
This article analyzes the agency of wealthy businessmen-politicians’ philanthropy foundations during the Ukrainian Maidan protests of 2013-14 in which crowdfunding and grassroots mobilization constituted key distinctive features. As the role of these philanthropy foundations remains obscure, this article aims to bridge this gap in our knowledge of Ukrainian politics and society. The protesters strived to achieve social change and democratization similar to what was being purported by wealthy businessmen-politicians’ foundations during the years leading up to the protests. However, since the protesters specified one particular aim as “de-oligarchization,” the involvement of these organizations is puzzling. What did these foundations do at this critical point? To what extent can their actions or inactions be explained by the institutional and framework constraints of the foundations, the strategies of the wealthy businessmen-politicians behind the foundations, and the lack of the foundations’ legitimacy in the eyes of the civic sector activists? The analysis covers different types of foundation and is based on semi-structured interviews involving the foundations’ representatives, think-and-do tank analysts, and Maidan activists, over the years 2011 to 2017. The findings show that the organizational entities were largely directed by their respective founders. This indicates a dependence of the philanthropic organization on the political affiliation of the founder, rather than on the framed ambition of the foundation. Similar to the impact of philanthropic organizations in other institutional contexts, the impact of philanthropy foundations on the Maidan social movement proved marginal. Since oligarchs could not be invisible during the political turmoil, they tried to retain a position from which they could deny responsibility for specific actions. The logic of commitment compensation and the logic of flexibility advanced by Markus and Charnysh proved useful for analyzing the strategies of these businessmen-politicians.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".