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Record W4384661997 · doi:10.22215/etd/2023-15616

An Imaginary Reconstruction of Blackness in Africville, Mapping and Place Making

2023· dissertation· en· W4384661997 on OpenAlexaboutno aff
Otmar George Melhado

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldArts and Humanities
TopicPhotography and Visual Culture
Canadian institutionsnot available
Fundersnot available
KeywordsNova scotiaThe ImaginaryContext (archaeology)InjusticeSocial injusticeResistance (ecology)Making-ofAcreSociologyGender studiesPolitical scienceAestheticsGeographyArtLawArchaeologyManagementPsychologyPoliticsEthnology

Abstract

fetched live from OpenAlex

Africville is a two-and-a-half-acre lot that is home to a northerly park in Halifax, Nova Scotia. It was a community that was razed for fifteen years, starting in 1964 after the Halifax City Council decided to rezone the land for industrial use. This decision eventually became psychologically hostile and regressive to the black citizens as it took shape in enacting dispossession and spatial injustice that brought with it several forms of racial marginalization strategies. Eventually, this seaside country village would cease to exist after its original formation in 1848. Through a counternarrative lens, the project explores forms of memory, re-memory, and resistance in making as speculative social interventions of a broader context of commemoration through black memorials and spaces. It includes utilizing pedagogical lessons learned in architectural design and conservation. This would position the project’s proposal towards reinterpreting the Afro-Canadian experience within a responsive and engaging and contemporary framework.

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)0.002
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: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.835

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0280.033
Scholarly communication0.0090.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.036
GPT teacher head0.292
Teacher spread0.256 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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