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Record W4415721473 · doi:10.1080/17511321.2025.2579518

Interview with Professor Hartmut Rosa

2025· article· en· W4415721473 on OpenAlexaff
Hartmut Rosa, Matthieu Quidu, Jérôme Visioli, Brice Favier-Ambrosini, Bernard Andrieu

Bibliographic record

VenueSport Ethics and Philosophy · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicGerman History and Society
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsAmateurCompetition (biology)Order (exchange)Empirical researchEducational research

Abstract

fetched live from OpenAlex

In this interview, Hartmut Rosa, author of Resonance and Pedagogy of Resonance, discusses the origins of his flagship concept of resonance, as well as the role of sport in its conceptualization. In particular, he talks about his personal and passionate relationship with sport, both as an amateur practitioner and an avid spectator. The philosopher emphasizes the fundamentally unavailable nature of resonance, which makes it possible for it to emerge in a very wide range of practices, including situations that might seem unlikely at first glance, such as self-quantification, competition or opposition. From an educational perspective, Rosa highlights the unequal dispositions to resonance among children, which must be understood by teachers and the education system in order to enable each student to discover their preferred axes of resonance. Finally, various empirical research perspectives to be developed on experiences and relationships of resonance are considered.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.002
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0060.015
Insufficient payload (model declined to judge)0.0300.012

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.

Opus teacher head0.079
GPT teacher head0.280
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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