Keeping One's Shiny Mercedes in the Garage: Why Higher Education Quantification Never Really Took Off in Germany.

Autor/in	Hillebrandt, Maarten
Titel	Keeping One's Shiny Mercedes in the Garage: Why Higher Education Quantification Never Really Took Off in Germany.
Quelle	In: Politics and governance, 8 (2020) 2, S. 48-57 PDF als Volltext kostenfreie Datei Link als defekt meldenVerfügbarkeit
Sprache	englisch
Dokumenttyp	online; Zeitschriftenaufsatz
ISSN	2183-2463
DOI	10.17645/pag.v8i2.2584
Schlagwörter	Hochschulpolitik; Hochschulverwaltung; Quantitative Daten; Datenverarbeitung; Deutschland
Abstract	The cybernetic dream of regulatory 'dashboard control' has taken off in the German higher education system. Both government regulators and university managers are engaged in the creation of waves of increasingly fine-grained quantitative data. Yet a wide range of recent case studies of the German higher education sector attest that in spite of this 'datafication' frenzy, the impact of the collected data mass on regulatory and managerial decision-making capacities seems to have remained relatively limited. This article explores why, in spite of the considerable investment in quantitative data infrastructures in the German higher education sector, this did not result in significant overt analytical capacity building. It explores three hypotheses: 1) a legal hypothesis according to which quantification is curbed by legal protections under the Rechtsstaat; 2) a dysfunctionality hypothesis which holds that decision makers reject quantification as a flawed and impracticable pursuit; and 3) an egalitarian federalism hypothesis which argues that Germany's federal states seek to prevent commensurability to avoid comparison and competition. The article finds that, in spite of its inconspicuousness, quantification indeed does inform various central decision-making processes. However, different legal, political, and relational factors prompt decision makers to engage in a hybrid, tempered and, overall, untransparent application of numerical data.
Erfasst von	GESIS - Leibniz-Institut für Sozialwissenschaften, Mannheim
Update	2022/3