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Record W3035271094 · doi:10.1177/1098214019899164

Talking Circles: A Culturally Responsive Evaluation Practice

2020· article· en· W3035271094 on OpenAlexaff
Martha A. Brown, Sherri Di Lallo

Bibliographic record

VenueAmerican Journal of Evaluation · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsStollery Children's Hospital
Fundersnot available
KeywordsPrivilege (computing)IndigenousInvisibilitySociologyPower (physics)StakeholderCulturally appropriatePower structurePsychologyPedagogySocial psychologyPublic relationsComputer scienceEthnographyPolitical scienceMedicineComputer securityArtificial intelligence

Abstract

fetched live from OpenAlex

Talking Circles are safe spaces where relationships are built, nurtured, reinforced, and sometimes healed; where norms and values are established; and where people connect intellectually, spiritually, and emotionally with other members of the Circle. The Circle can also be an evaluation method that increases voice, decreases invisibility, and does not privilege one worldview or version of reality over another. The purpose of this article is to describe how the Circle can be a culturally responsive evaluation practice for those evaluators wishing to build relationships, share power, elicit stakeholder voice, solve problems, and increase participants’ capacity for program design, implementation, and evaluation. Circles can be used by both Indigenous and non-Indigenous evaluators. By offering the global evaluation community this concrete, practical, and culturally responsive approach, we open the door so that others can build on this work and offer additional insights as this practice is used, refined, and documented.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptno category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
grokno category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptualhigh
opusno category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptualmedium
models splitAgreement compares identical category sets and study designs across arms.

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)0.180
metaresearch head score (Gemma)0.162
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: none
Teacher disagreement score0.180
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1800.162
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0140.022
Scholarly communication0.0180.015
Open science0.0050.021
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0100.003

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 head0.221
GPT teacher head0.544
Teacher spread0.322 · 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

Labeled directly by 3 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designQualitative · Theoretical or conceptual
Domainnot available
GenreMethods

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

Citations64
Published2020
Admission routes1
Has abstractyes

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