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Record W4390012083 · doi:10.24908/jcri.v10i2.17052

Reflections on The Remember Me Project: Queen’s University’s Black Past and the AfroWomanist Sankofa Archive to Our Future

2023· article· en· W4390012083 on OpenAlexafffundvenue
Elizabeth Peprah-Asare

Bibliographic record

VenueJournal of Critical Race Inquiry · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsQueen's University
FundersQueen's University
KeywordsQueen (butterfly)SociologyPoliticsAfrican-American historyInclusion (mineral)Diversity (politics)Media studiesGender studiesLawAnthropologyPolitical science

Abstract

fetched live from OpenAlex

How does one endeavour to recover history that has been intentionally hidden, erased, and forgotten? In what ways can the ethics of Equity, Diversity, and Inclusion (EDI) be applied within archival processes? In this article, I highlight the work I recently completed under the supervision of the Director of Equity, Diversity, Inclusion, and Indigeneity (EDII) at Queen’s University as an “Afrofuturist” Graduate Research Fellow in a project titled The Remember Me Project: Black Historic Lives and Our Future(s) at Queen’s University. I focus on one poem from my collection titled, “Alfie or Did I Really Do Alright?” I problematize the now infamous figure of Alfred “Alfie” Pierce, a Black male employee of Queen’s Athletics department to problematize the ways Black stories are forgotten, lost, and devalued within academic institutions. In simultaneously speaking back to the politics of whose stories are worthy of remembrance at Queen’s, the horrors of “Alfie’s” story, and the difficulties involved in Black archival recovery, I introduce my intervention “the AfroWomanist Sankofa Archive” as an interdisciplinary framework constructed as a guideline for African-heritage peoples to utilize while navigating dis-empowering research material involving African archival information. I end with some recommendations for our Afrofuture.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.904
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.064
GPT teacher head0.397
Teacher spread0.334 · 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.

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

Citations0
Published2023
Admission routes3
Has abstractyes

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