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Record W4394938097 · doi:10.4236/ce.2024.154033

The Natural Environment in Social Work Curriculum: A Narrative Reflection of Teaching-Learning through a Sustainability Course Design and Delivery

2024· article· en· W4394938097 on OpenAlexaff
Somnoma Valerie Ouedraogo

Bibliographic record

VenueCreative Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsMacEwan University
Fundersnot available
KeywordsSustainabilityCourse (navigation)Reflection (computer programming)CurriculumNarrativeNatural (archaeology)Work (physics)SociologyPedagogyMathematics educationComputer scienceEngineering ethicsPsychologyEngineeringGeographyArtEcologyBiology

Abstract

fetched live from OpenAlex

This paper aims to narrate the author’s journey about a newly designed course called, Social Work and Sustainability offered at the beginning of social work education, which she used to develop and improve a learning material to enhance her teaching. It is a narrative reflection centered on class observations (from 2016 to 2019) and integrates a discussion of research related to sustainability in higher education. The author used both worldview and narrative methodological approaches to reflect on the course design and pedagogy process. Then Zapf’s (2008) model of the person as environment is expanded to lay out the evolution of the traditional ecosystems’ framework with the interconnectedness model framework of sustainability. The study highlights the importance of decolonization approaches and transgressive pedagogy calling for the development and implementation of Sustainable Social Work from a culturally-grounded perspective. The study recommends social workers to advocate for a reconnection with the natural environment through the integration of environmental awareness into theoretical and practical aspects of social work.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
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 head0.025
GPT teacher head0.395
Teacher spread0.370 · 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.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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