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Record W3035293599 · doi:10.36510/learnland.v13i1.1013

Performing Life Stories: Hindsight and Foresight for Better Insight

2020· article· en· W3035293599 on OpenAlexvenueno aff
Lynn Norton, Yvonne Sliep

Bibliographic record

VenueLEARNing Landscapes · 2020
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsnot available
FundersInyuvesi Yakwazulu-NataliNational Institute for the Humanities and Social Sciences
KeywordsReflexivityDialogical selfHindsight biasAgency (philosophy)PerceptionPerformativityFutures studiesPower (physics)Field (mathematics)SociologyPsychologyAestheticsPedagogySocial psychologySocial scienceComputer scienceGender studies

Abstract

fetched live from OpenAlex

We examine the benefits of developing critically reflexive learners through life story performance embedded in a Critical Reflexive Model. Students are invited to work with their life stories in a safe, dialogical space and to deconstruct various forms of power and its influence on their lives. Using a mix of creative and embodied methodologies, students explore their values, agency, and performativity to enable a deeper level of critical reflexivity. As researchers, we track what ongoing contributions reflexivity has made to the lives of students after graduating and currently working in the field as professionals. Our findings indicate that students experience shifts in their perception of self, others, and their contexts, which make them better placed to respond to the many complexities of society in South Africa on both a personal and a professional level.

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)0.016
metaresearch head score (Gemma)0.043
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: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.043
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.011
Scholarly communication0.0150.018
Open science0.0020.012
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0100.002

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 head0.051
GPT teacher head0.333
Teacher spread0.282 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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