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Record W2891788907 · doi:10.1089/eco.2018.0059

Unsettling Ecopsychology: Addressing Settler Colonialism in Ecopsychology Practice

2018· article· en· W2891788907 on OpenAlexaff
Alysha Tylynn Jones, David S. Segal

Bibliographic record

VenueEcopsychology · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsVictoria Heart Institute FoundationGolder Associates (Canada)
Fundersnot available
KeywordsIndigenousColonialismSociologySolidarityEnvironmental ethicsAccountabilityPrivilege (computing)Field (mathematics)PhenomenonEpistemologyPolitical scienceEcologyPoliticsLawPhilosophy

Abstract

fetched live from OpenAlex

This article identifies settler colonialism as a phenomenon existing outside of awareness in the field of ecopsychology and begins to explore what “unsettling” ecopsychology may entail. Unsettling the field is a process of revealing how ecopsychology reproduces and reinforces settler colonialism. This process requires deep reflection among practitioners regarding how they can challenge the dominant colonial narratives that underpin settler privilege within the field itself. Offered as additional points of engagement in the process of unsettling are practices of accountability and relationality through the learning of history and cultural protocols and engagement in acts of solidarity with Indigenous land-based resurgence. By opening up this dialogue, we (the authors) seek to make a critical contribution to the field of ecopsychology and, as non-Indigenous/settler practitioners, to encourage a discussion of accountability for those doing therapeutic land-based nature connection work as visitors on traditional Indigenous territories.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.044
GPT teacher head0.430
Teacher spread0.385 · 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; both teacher heads agree on what is shown here.

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

Citations16
Published2018
Admission routes1
Has abstractyes

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