MétaCan
Menu
Back to cohort
Record W2470105771 · doi:10.7202/1070468ar

The Beautiful Risk of Education (Gert Biesta)

2020· article· en· W2470105771 on OpenAlexaffvenue
Doron Yosef‐Hassidim

Bibliographic record

VenuePhilosophical Inquiry in Education · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience, Education and Cognitive Function
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEpistemologyPhilosophySociologyMathematics educationPsychology

Abstract

fetched live from OpenAlex

Biesta's work in last two decades is of the kind that is too often lacking in philosophy of education discourses, or in foundations of education in general: examination that takes education itself, as a whole, as its "unit of analysis," and focuses on what education should mean. The Beautiful Risk of Education continues Biesta's efforts to articulate what in hindsight he refers to as a "theory of education" (p. xi), a theory not only in the descriptive sense but primarily in the normative one. This book is the latter in what can be seen as a series of three books by Gert Biesta. In Beyond Learning: Democratic Education for a Human Future (2006) he develops subjectivity as a central dimension in education through a critique of the dominant discourse of learning in education and an exploration of the notion of "coming into presence." In Good Education in an Age of Measurement (2010) he introduces a broader education framework by adding two other dimensions, qualification and socialization. In The Beautiful Risk of Education (2014) Biesta "focuses on a theme that was implicit in the other two books" (p. x), namely the weakness of education. Biesta believes this theme deserves explicit examination since it has important implications that might assist in engaging with his ideas in practical settings. This review is a good opportunity to take a comprehensive look at Biesta's work, after completing the 'trilogy'.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.092
GPT teacher head0.344
Teacher spread0.251 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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
Published2020
Admission routes2
Has abstractyes

Explore more

Same venuePhilosophical Inquiry in EducationSame topicNeuroscience, Education and Cognitive FunctionFrench-language works237,207