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Record W2992358871 · doi:10.14288/tci.v5i2.29

A pedagogy of mourning: tarrying with/in tragedy, terror, and tension

2006· article· en· W2992358871 on OpenAlexaff
Monica Waterhouse

Bibliographic record

VenueOpen Collections · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicPeace and Human Rights Education
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsNarrativeSociologyPassionsTragedy (event)Transformative learningAestheticsPedagogyEpistemologyLiteraturePhilosophySocial scienceArt

Abstract

fetched live from OpenAlex

In this text I offer a narrative reflection, as a teacher-traveler, on my live(d) experiences in a sometimes (always already) violent world. Preoccupied with the possibilities of the work of mourning, in the first two movements I draw on stories of my time teaching in China at the dawn of this millennium to tell tales of tragedy, terror, and tension that provoked strange pedagogical moments. I reflect on the difficulties and passions of live(d) pedagogies that crack open curri/culum to tarry with/in these provocative, generative spaces as places for mourning and connection, ambivalence and ambiguity. During the 3rd movement I join Butler’s questioning of ‘what counts as a grievable life’ as I attempt a textual encounter with an (un)grievable ‘other’. I ask: How might recognition of lives/deaths through the act of inscription and collective mourning be related to understanding human connection? In a pedagogical refrain, I draw Derrida into my conversation with feminist theory to ask seriously about the pedagogical potential of tarrying with/in mourning, particularly for peace education that prioritizes an awareness of human connection. What are the transformative possibilities of returning again to mourning? I conclude with a call from peace educators and other emerging epistemologies that challenge us to think differently about human connection in our pedagogical work. Above all this text is a provocation in/to difficult spaces of mourning and pedagogical movement.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.350
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.027
GPT teacher head0.337
Teacher spread0.310 · 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.

Study designNot applicable
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

Citations1
Published2006
Admission routes1
Has abstractyes

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