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Record W153664728

Postcolonial bildungsroman: indigenous subjects and identity-formation

2007· article· en· W153664728 on OpenAlexaboutno aff
Clare Bradford

Bibliographic record

VenueDeakin Research Online (Deakin University) · 2007
Typearticle
Languageen
FieldArts and Humanities
TopicThemes in Literature Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousIdentity (music)NegotiationSociologyGender studiesPower (physics)Identity negotiationPoliticsWhite (mutation)AestheticsArtPolitical scienceSocial scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

\n\t\t\t\t\tIn the bildungsroman as it has conventionally been defined, individuals attain self-actualisation through a series of experiences whereby they accommodate their individuality to a social order.  Protagonists negotiate their transition from childhood to young-adulthood by way of educative experiences, trials of various kinds, and a search for identity, which is generally formulated as a fixed or stable essence which they must discover or accept.  In this paper I focus on two examples of bildungsroman by Indigenous Canadian and Australian authors: Jeannette Armstrong’s Slash (1985),and Richard J. Frankland’s Digger J. Jones (2007).  Both novels feature male protagonists whose stories play out against the background of Indigenous activism in the 1960s.  As they track the identity-formation of their protagonists, the two novels deconstruct simple or fixed ideas of national identity by pointing to the complex cross-cultural relationships which have characterised settler societies.  At the same time,these novels dramatise the power of socialising practices which promote white superiority and position Indigenous peoples as supplicants or victims.  Both novels draw on what Paul Havemann terms the "new politics of identity and cultural recognition" which characterise contemporary Indigenous activism, re-reading events and settings of the 1960s in the light of discourses of cultural recognition and self-determination.<br />\n\t\t\t\t

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.985
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.318
Teacher spread0.277 · 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 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

Citations2
Published2007
Admission routes1
Has abstractyes

Explore more

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