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Record W4211040625 · doi:10.1111/area.12786

Two‐eyed‐seeing/<i>Etuaptmumk</i> in the colonial archive: Reflections on participatory archival research

2022· article· en· W4211040625 on OpenAlexafffundabout
Declan Cullen, Heather Castleden

Bibliographic record

VenueArea · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Victoria
FundersInstitute of Aboriginal Peoples Health
KeywordsColonialismIndigenousNarrativeCitizen journalismNegotiationSovereigntySociologyParticipatory action researchSketchMedia studiesHistoryPolitical scienceAnthropologySocial scienceLawPoliticsLiteratureArt

Abstract

fetched live from OpenAlex

Abstract Due to their origins and purpose, institutional archives present us with a set of limits when we seek to use them in struggles against colonialism. Here we explore to what extent we can negotiate those limits and practice participatory historical research. In particular, we explore how our project's direction by Indigenous methodology, a Two‐Eyed Seeing approach, influenced our relationship with the colonial archive. The research reported on in this paper involved establishing a historical narrative from Canadian federal and provincial colonial archives. This narrative was used in conjunction with oral histories from and interviews with Elders and Knowledge‐holders of the Mi'kmaq Nation to document the emergence of social assistance policy as a central aspect of colonial processes. Our historical research was, thus, explicitly tied to understanding the Mi'kmaw struggle for sovereignty in the colonial present. Here we sketch out aspects of our attempt to navigate between the colonial past and colonial present using a participatory approach to the historical geographies of Indigenous–settler relations.

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)0.055
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.954
Threshold uncertainty score0.460

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0550.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0460.072
Scholarly communication0.0190.009
Open science0.0030.016
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0050.000

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 head0.189
GPT teacher head0.476
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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations6
Published2022
Admission routes3
Has abstractyes

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