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Record W4394564003 · doi:10.22584/nr55.2024.010

Review of Decolonizing Data: Unsettling Conversations about Social Research Methods (by Jacqueline M. Quinless)

2024· article· en· W4394564003 on OpenAlexaffvenueabout
Sara McPhee-Knowles, Lisa Kanary

Bibliographic record

VenueThe Northern Review · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsYukon University
Fundersnot available
KeywordsSociologyAnthropology

Abstract

fetched live from OpenAlex

Unsettling Conversations about Social Research Methods, is a short, succinct volume that begins with the premise of examining the ways in which research practices contribute to colonization, and illustrates how social research can be part of "two-eyed seeing" (80) that incorporates Western and Indigenous values and world views.Quinless is a non-Indigenous scholar with extensive experience working with Indigenous communities.Th e fi rst chapter begins with an anecdote describing her research experiences in Inuvik, Northwest Territories, fi rst as a junior researcher with the federal government where timelines and objectives for her project were strict and clear, but she felt she was not "connected with people in the community in a meaningful way" (3).She contrasts this with a much more recent experience, also in Inuvik, that prioritized building relationships as part of the research process.From here, Quinless introduces the concepts of power, place, and relational responsibility in research design.In the most interesting part of the fi rst chapter, Quinless extensively cites Indigenous scholars in a discussion of Indigenous perspectives of well-being: in contrast to Western perspectives, the Indigenous concept of "the good life" is holistic and focuses on the balance between mental, physical, social, and emotional realms, as well as relation with the land and the water.As the author succinctly notes, "Mino-Bimaadiziwin goes well beyond income and education levels, housing and labour force activity (Newhouse & Fitzmaurice, 2012), which are how the Canadian state defi nes and measures well-being for Indigenous communities" (11).Th is contrast between Indigenous perspectives of well-being and defi cits-based health indicators emerging from Western research practices is a core theme of the book.Th e fi rst chapter introduces the Community Well-Being Index (CWB) and provides a critical examination: the CWB primarily focuses on income, education, housing, and labour force activity, neglecting crucial elements of physical, mental, spiritual, and emotional well-being.In fact, the well-being scores from the CWB

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)0.233
metaresearch head score (Gemma)0.426
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score0.946

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2330.426
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0140.012
Science and technology studies0.0110.032
Scholarly communication0.0170.026
Open science0.0060.012
Research integrity0.0090.024
Insufficient payload (model declined to judge)0.0040.004

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 head0.637
GPT teacher head0.706
Teacher spread0.069 · 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.

Study designNot applicable
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

Citations0
Published2024
Admission routes3
Has abstractyes

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