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Record W2894116866 · doi:10.18733/cpi29402

Decolonizing Reflexive Practice Through Photo Essay Aisinai’pi Storying Place

2018· article· en· W2894116866 on OpenAlexaffvenueabout
Christine A. Walsh, Natalie St-Denis, Anita Eagle Bear

Bibliographic record

VenueCultural and Pedagogical Inquiry · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsStorytellingReflexivityIndigenousDecolonizationOppressionIdentity (music)SociologyNarrativeAestheticsPolitical scienceAnthropologyArtPoliticsLawLiterature

Abstract

fetched live from OpenAlex

In response to the Truth and Reconciliation Commission’s Calls to Action, universities across Canada are currently exploring ways to decolonize and indigenize their institutions and curriculum. The profession of social work has had an historical and ongoing role in the oppression of Indigenous Peoples, and now has the responsibility to advance and integrate Indigenous worldviews for reconciliation and healing. Storytelling has been described as an embodiment of Indigenous knowledges and validates the experiences of Indigenous Peoples. Although traditional stories have been most often shared orally, visual methods of storytelling have gained popularity among oppressed communities as a way to share their realities. This photo essay project was developed as a tool to guide social work educators and students to decolonize their reflexive practice by reflecting on their personal and professional identities in relationship to place.The photo essay presents a series of images evoking stories of original peoples and settlers on this land and fuels important questions about identity and belongingness. Keywords: Decolonization, reflexive practice, photography, storytelling

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)0.002
metaresearch head score (Gemma)0.004
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0050.011
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.686
GPT teacher head0.584
Teacher spread0.102 · 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 designQualitative
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

Citations4
Published2018
Admission routes3
Has abstractyes

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