MétaCan
Menu
Back to cohort
Record W2297559464 · doi:10.18733/c3v88m

Reviving reasonableness: Expansive reason-giving and receiving for global social justice education

2013· article· en· W2297559464 on OpenAlexaffvenue
Derek Tannis

Bibliographic record

VenueCultural and Pedagogical Inquiry · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsExpansiveRelation (database)EmotiveEpistemologyCosmopolitanismAssertionAction (physics)Meaning (existential)SociologyEconomic JusticeTypologySocial psychologyPsychologyPolitical scienceLawPhilosophyPolitics

Abstract

fetched live from OpenAlex

Reasonableness is a term that is used widely in relation to global social justice, yet its meaning differs depending on its theoretical foundations. In this paper, I examine the breadth of these meanings, focusing on the pedagogical significance of reasonableness as something that is assessed, recognized and enacted. I present a model of reasonableness that expands upon Erman’s (2007) concept of reason-giving and is founded upon the philosophy of inter-subjective recognition as described by Honneth (1996) and the idea of capabilities as theorized by Sen (2009) and Nussbaum (2005). I develop a typology of reason-giving and reason-receiving, including arbitrary, emotive, authoritative, tentative and expansive analytical-relational modes. I conclude that the assertion of another person’s reasonableness / unreasonableness may be viewed as an inter-subjective and intercultural lived relation. Approximating the cosmopolitanism proposed by Nussbaum (2005) and Appiah (2006), I propose that we should aim to create learning approaches and environments that foster exploratory and compassionate reason-giving and receiving. In an era of global social justice discourse and action, I argue that cultivating a reflective approach to reason-giving and receiving would develop in students an expansive conception of and capacity for reasonableness.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.441
GPT teacher head0.507
Teacher spread0.066 · 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 designQualitative
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

Citations0
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueCultural and Pedagogical InquirySame topicGlobal Education and MulticulturalismFrench-language works237,207