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Record W4400965226 · doi:10.53103/cjlls.v4i4.172

Nature’s Daughters: Empowerment and Environmental Stewardship in Barbara Kingsolver’s Prodigal Summer and Flight Behavior

2024· article· en· W4400965226 on OpenAlexvenueno aff

Bibliographic record

VenueCanadian Journal of Language and Literature Studies · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicEcocriticism and Environmental Literature
Canadian institutionsnot available
Fundersnot available
KeywordsEmpowermentStewardship (theology)Environmental stewardshipEnvironmental ethicsSociologyPolitical scienceEnvironmental resource managementEnvironmental scienceLawPhilosophy

Abstract

fetched live from OpenAlex

In the twenty-first century, women are empowered through natural elements, and they nurture and preserve their environment.'Nature's Daughters' likely refers to the female protagonists in Barbara Kingsolver's novels Prodigal Summer and Flight Behavior, who demonstrate a strong bond with nature and play significant roles in environmental activism and conservation efforts.These characters embody the interconnectedness between women, nature, and empowerment, reflecting the idea of women as agents of change in ecological stewardship.The first discussion concerns how female protagonists, Deanna, Lusa, and Nannie, navigate challenges and empower through nature while preserving the natural environment and its beings in Kingsolver's Prodigal Summer.The second discussion concerns how the female protagonist, Dellarobia, tackles her adversities and is empowered through the migrated monarch butterflies and the conservation of the endangered species in Kingsolver's Flight Behavior.Kingsolver highlights the themes of empowerment, oppression, Sustainable environmental management, and discrimination in society.Female characters have an intimate relationship with nature rather than men because women have the role of mothers who care for or protect family members, natural beings, and their surroundings.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score0.581

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 communication0.0010.000
Open science0.0000.000
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 head0.010
GPT teacher head0.237
Teacher spread0.227 · 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 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
Published2024
Admission routes1
Has abstractyes

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