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Record W2979587583 · doi:10.22329/csw.v11i3.5833

Sustainability, human rights, and environmental justice

2019· article· en· W2979587583 on OpenAlexvenueno aff
Catherine Hawkins

Bibliographic record

VenueCritical Social Work · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityEnvironmental ethicsAction (physics)Human rightsEconomic JusticeSocial sustainabilityEnvironmental justiceWork (physics)SociologySustainable developmentSocial justicePolitical sciencePublic relationsPerspective (graphical)Engineering ethicsLawLaw and economicsEcologyEngineering

Abstract

fetched live from OpenAlex

Social work has a long-standing tradition of emphasizing the interaction of people and their environment, although this systems perspective has focused almost exclusively on the importance of social relationships. There is an emerging emphasis within the profession regarding the need to pay more attention to the critical role of the physical environment. The last fifty years has seen a growing global ecological movement, and the profession is joining the call to action for sustainability. Social work must extend this mission to include environmental justice, the human right to live in a clean, safe, and healthy environment. The world’s most poor, vulnerable, and oppressed people often live in the most degraded environments and have no control over resources. The important connections between social work, sustainability, human rights, and environmental justice in our contemporary world need to be more clearly articulated in the scholarly literature. An understanding of these separate but closely linked concepts is necessary for the profession to effectively pursue the goal of making the world a more just, humane, and sustainable home for all life.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0080.049
Scholarly communication0.0090.006
Open science0.0010.009
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0070.000

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 head0.016
GPT teacher head0.342
Teacher spread0.326 · 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 designTheoretical or conceptual
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

Citations45
Published2019
Admission routes1
Has abstractyes

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