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Record W4411107609 · doi:10.1108/edi-10-2024-0482

Rurality and intersectionality: a literature review

2025· review· en· W4411107609 on OpenAlexaff
Sarah Redshaw, Catherine Thomas, Nathan Kerrigan, Branka Krivokapic‐Skoko, Susan Flynn

Bibliographic record

VenueEquality Diversity and Inclusion An International Journal · 2025
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsTrinity College
Fundersnot available
KeywordsRuralityIntersectionalitySociologyGender studiesGeographyPolitical scienceRural area

Abstract

fetched live from OpenAlex

Purpose The paper presents a literature review conducted to consider the range and focus of papers applying a stated intersectional framework to rural contexts. Design/methodology/approach With a specific interest in intersectionality studies that were connected to rural areas, a number of databases were searched for the term “intersectionality”, and from 492 identified papers, 21 papers met the criteria for review. Thematic analysis captured the range of themes within and across each paper. Findings Although all papers considered gender, race and their relation to identity, the strongest theme throughout was the concept of place. Place was often related to how identity is shaped within place. Multiple inequalities and intersecting identities related to race, ethnicity, class, sex and place, and their impacts were documented. The extent to which intersectionality was able to be employed in analysis and discussion is highlighted. The papers sought to acknowledge the complexity in these domains with some providing in-depth analysis of experiences in a number of domains and examining norms, values, power structures and the discourses and narratives that support these. Research limitations/implications This literature review discussed papers from the Global North. It was imperative to consider nations with similar systems and governance sophistication to undertake meaningful analysis. Future research could encompass articles from across the globe (specifically, from areas and regions of the Global South) to compare and contrast applications and interpretations of intersectional research and practice in more varied contexts. There could also be a greater focus on historical debates that have influenced the interaction of intersectionality and rurality such as feminist approaches as well as more focus on confronting privilege and how that frames analyses. Practical implications Intersectionality requires application as a complete framework to research and practice so as to better hear the voices expressing lived experiences of individuals, groups and communities within all social identifiers of which place is a vital component. This is further compounded when considering the impact of interpretations of rurality. The authors of this literature review acknowledge a need to de-whiten and decolonialise experiences encapsulated in the notions, concepts and application of intersectionality and rurality. Capturing the complexity that emerges in intersectional analysis is a challenge that has been embraced to varying degrees within the papers reviewed. Social implications Appreciation of the complex array of factors that contribute to rural contexts needs to be embraced in research through intersectional analysis. What is absent from some of the papers, is an explanation or need to challenge the urban-centric and white-dominated views of intersectionality and the application of intersectionality excluding other social indicators such as the impact of place. The notion of place within itself incorporates the social and without the social, then place would become merely space (Johnston, 2018). Originality/value The papers chosen presented a range of applications of intersectionality that allow us to consider an intersectional lens with a strong application indicating the use of interrelated themes throughout such as race and gender in relation to place and power structures.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0010.014
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 head0.045
GPT teacher head0.333
Teacher spread0.287 · 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 designOther design
Domainnot available
GenreReview

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

Citations6
Published2025
Admission routes1
Has abstractyes

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