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Record W4312058281 · doi:10.2478/aa-2022-0009

Forging a space for dialogue and negotiation in modern picture books by Melanie Florence

2022· article· en· W4312058281 on OpenAlexaboutno aff
Susana Amante

Bibliographic record

VenueArs Aeterna · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicThemes in Literature Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMultitudeNegotiationIndigenousSpace (punctuation)UnisonDualismVoiceSociologyIdentity (music)Media studiesHistoryGender studiesAestheticsPolitical scienceLinguisticsArtLawSocial scienceEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

Abstract Canadian children’s literature has a relatively short history, which is not surprising because Canadian literature itself is a recent and problematic category, struggling for a definition and identity of its own. The lack of national homogeneity is reflected in both CanLit and its counterpart for children, and rather than being a weakness, the multitude of voices that inhabit the Canadian territory has become its essence and strength. Lately, we have noticed a growing interest and market demand for picture books by Indigenous voices. Melanie Florence is one such voice, and she honours her past by bringing to the fore the inescapable dark weight of collective tragedies such as the residential school system and the disappearance and murder of Aboriginal women and girls, a hidden national crisis. In this article, we aim at getting to know and help readers discover Missing Nimâmâ and Stolen Words by this new picture book writer, who is speaking up and voicing First Nations’ concerns, bringing back memories, but also forging a space for dialogue and negotiation, a space where text and illustration are combined and provide a harmonious whole. In this space, difference and binarisms do not result in dualism, but in highly synergistic relationships.

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 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 score0.739
Threshold uncertainty score0.356

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 studies0.0000.000
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 head0.011
GPT teacher head0.207
Teacher spread0.196 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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