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Record W4249193010 · doi:10.32920/ryerson.14654457.v1

A Journey of Learning: Relations With the Land, Environmental Violence and Dispossession and Decolonizing Social Work

2021· preprint· en· W4249193010 on OpenAlexaff
Lauren Anderson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicAfrican cultural and philosophical studies
Canadian institutionsToronto Metropolitan UniversityWestern University
Fundersnot available
KeywordsIndigenousDecolonizationSociologyColonialismProcess (computing)Work (physics)Natural (archaeology)Environmental ethicsPolitical scienceEcologyGeographyPoliticsEngineeringLawArchaeology

Abstract

fetched live from OpenAlex

For the purposes of this Major Research Paper (MRP), I have chosen to situate my project in a position of intentional engagement with attempts to challenge settler colonialism. Toward this goal, I attempt to engage in learning about ways in which social work can engage in intentions of decolonizing. As a woman of mixed ancestry – both of settler and Haudenosaunee background – this MRP uses the Petal Flower Framework (Absolon, 2011) to support the process of learning of relations with Creation (the natural environment), with consideration of social work practice, and the ongoing systems which perpetuate violence against the environment. The journey involved in this process has included intentional thought in attempt to learn from Indigenous authors as I strive to decolonize my personal and professional journeys.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0220.073
Scholarly communication0.0160.012
Open science0.0010.014
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.276
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), 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
Published2021
Admission routes1
Has abstractyes

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