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Record W4285033404 · doi:10.4324/9781003205296-5

Storying Vulnerability

2022· book-chapter· en· W4285033404 on OpenAlexaboutno aff
Jessica Vorstermans

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsnot available
Fundersnot available
KeywordsVulnerability (computing)Computer scienceComputer security

Abstract

fetched live from OpenAlex

This chapter takes up the learning of Northern student participants with a small international experiential service learning (IESL) organization, Intercordia Canada, that worked with Canadian universities and operated on a relational model rooted in a philosophy of “being with, not doing for” and used innovative pedagogical tools to counter the hegemonic charity-steeped benevolent Northerner narratives characteristic of the field of IESL. It focuses on student reflections (stories) about vulnerability during their Global South IESL placements and works to understand how participants experienced encounters that are structured to challenge capitalist forms of community. The deep emotional labour of naming and announcing one’s own vulnerability challenges the script of the benevolent Northern helper in the field of IESL. In the pedagogy of Intercordia, vulnerability was constructed as a natural part of our interconnected humanness and was employed as a tool to get into the messy and difficult work of creating relationships of mutuality across difference. The chapter takes up the ways these tools can reproduce a dangerous equating of vulnerability and pain, and offerings of ways that this thinking can be interrupted. IESL programming often employs the ascribing of vulnerable to the Southern other and need for the Northerner to help, rehabilitate, save, and care for that vulnerable other . The unruly body is always the Southern disabled body, helped and brought joy to by the Northern helper. The author argues that the pedagogy of Intercordia intervened in this construction and invited Northern student participants to engage deeply with their own vulnerability, discovering the ways they are vulnerable and posited that through this self-reflexive process mutuality in relationship with the other is possible.

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.019
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0190.033
Scholarly communication0.0110.018
Open science0.0020.019
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0120.002

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 head0.043
GPT teacher head0.306
Teacher spread0.263 · 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 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
Published2022
Admission routes1
Has abstractyes

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