Automated Organization Profile
Institute of Philosophy and Sociology of the Polish Academy of Sciences

Current S-Index

1.4

Sum of Dataset Indices for all datasets

Average Dataset Index per Dataset

0.7

Average Dataset Index per dataset

Total Datasets

Total datasets in this organization

Average FAIR Score

15.4%

Average FAIR Score per dataset

Total Citations

Total citations to the organization's datasets

Total Mentions

Total mentions of the organization's datasets

S-Index Interpretation

The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.

What it means:

A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
It provides a single number to track your research data impact over time

Current S-Index: 1.4 (sum of 2 datasets Dataset Index scores)

More information here.

S-Index Over Time

Cumulative Citations Over Time

Cumulative Mentions Over Time

Datasets

Replication Data for: The Emergence of Right-Wing Partisanship in Poland, 1993–2018: Reconciling Demand-Side Explanations of the Success of Illiberalism (Version: 1.0)

Replication data for "The Emergence of Right-Wing Partisanship in Poland, 1993–2018: Reconciling Demand-Side Explanations of the Success of Illiberalism"

Authors

Ślarzyński, Marcin

1 Citation0 Mentions15% FAIR0.7 Dataset Index

10.7910/dvn/hqtz6eJanuary 2023

Replication Data for: Stalling the state: How digital platforms contribute to and profit from delays in the enforcement and adoption of regulations (Version: 1.0)

While it is often claimed that the pace of digital transformation is such that its own, often glacial changes do not allow the state to catch up, we argue that technological companies, with the help of some state actors, have been slowing the state down. To capture this phenomenon, we introduce the notion of stalling strategies. We argue that stalling strategies have allowed digital platforms to create time that they have spent generating revenue and accumulating platform power, which later protected them from state actions. Drawing on a case study of Uber in Poland and a number of shadow cases, we distinguish five stalling strategies: reinventing classifications, stealing the time of street-level bureaucrats, dragging out court proceedings, delaying new regulations, and taking time to (not) comply. By analyzing stalling strategies, this article contributes to discussions about the politics of platform capitalism, the temporality of digitalization, and institutional drift.