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Record W2039125572 · doi:10.1080/026037042000317347

<i>A Fine Balance</i>in truth and fiction: exploring globalization’s impacts on community and implications for adult learning in Rohinton Mistry’s novel and related literature

2005· article· en· W2039125572 on OpenAlexaffabout
Kaela Jubas

Bibliographic record

VenueInternational Journal of Lifelong Education · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGlobalizationSociologyRhetoricReading (process)Media studiesLifelong learningPublic relationsPedagogyPolitical scienceLawLinguistics

Abstract

fetched live from OpenAlex

Abstract Globalization continues to interest researchers and practitioners as it unfolds around us. This article contributes to the analysis of globalization's discourse, objectives and outcomes, by exploring the impact of globalization on community and its implications for adult learning. Using selected themes from a work of fiction to frame this exploration, the article asserts that the study of fiction can bolster critical thinking and learning. Excerpts from Rohinton Mistry's novel, A Fine Balance, initiate an investigation of globalization's rhetoric of promise and connectedness, and introduce a review of related research and other non‐fictional writings. The incorporation of fiction into this analysis attempts to demonstrate that a complex, often technical topic such as globalization can be articulated in a way that is accessible to a broad community of formal and informal adult learners. The article concludes that globalization disrupts community and social capital, despite the increasing recognition of their role in supporting lifelong learning. Acknowledgements This article grew out of a paper produced for a directed reading course in my Masters programme. I thank Dr Thomas Sork for supervising my work during this course and for his generous support, feedback and editing assistance throughout the process of producing and refining this article. I also thank Dr Sunera Thobani for early discussions about the topic of this paper. Notes Kaela Jubas is a doctoral student in the Educational Studies Programme at The University of British Columbia, 2125 Main Mall, Vancouver, BC, V6T 1Z4, Canada; e‐mail: kaelaj@interchange.ubc.ca Additional informationNotes on contributorsKAELA JUBAS Footnote Kaela Jubas is a doctoral student in the Educational Studies Programme at The University of British Columbia, 2125 Main Mall, Vancouver, BC, V6T 1Z4, Canada; e‐mail: kaelaj@interchange.ubc.ca

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.025
GPT teacher head0.344
Teacher spread0.319 · 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 designObservational
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

Citations13
Published2005
Admission routes2
Has abstractyes

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