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Record W6943827080 · doi:10.17613/6k8c3-9ca37

African Architecture Matters: Developing and Increasing the Visibility of African Architectural Collections in North America and Abroad

2022· article· en· W6943827080 on OpenAlexaff

Bibliographic record

VenueKnowledge Commons (Lakehead University) · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicArchitecture, Art, Education
Canadian institutionsCanadian Armed Forces
Fundersnot available
KeywordsContext (archaeology)ArchitectureVisibilityInstitutionFocus (optics)Donation

Abstract

fetched live from OpenAlex

While it is easy to focus on collection strengths, recently the CCA has tried to articulate where our collection is lacking. This is to understand why we historically focused on certain architectural styles and geographical areas, and to identify where to shift priorities to address these imbalances. We recently received a donation of nearly 3000 volumes from an NGO with consulting projects in Africa. These materials were collected by European planners and architects active in Africa. This collection is important for our institution and has spurred new projects and lines of inquiry. We hosted a Master's Studies Program in 2017 focusing on the study of 20th c. African architecture as a means to inform 21st c. building practices on the continent. In 2019 we launched a research program on post-colonial architectural perspectives in Africa. This program was developed in consultation with expert advisors and is meant to be a way to contextualize, re-imagine, and update our institution's collection. In this presentation, we shared how we are building a more global collection, while remaining sensitive to a growing movement to keep cultural property where it was created. Crucial to our discussion is placing this material within its context of creation and in dialogue with similar collections globally. By engaging the original collectors as consultants and inviting architects and scholars to discuss these materials, we can enrich and build upon these collections. Similarly, by creating programs that encourage scholars to use these collections, we gain a more thorough understanding of these materials and make connections with our existing collections. We are adopting these strategies for the sake of enriching the context of these collections and creating a dialogue between our collections and community, both within our walls and globally. Presented as part of the panel "Thinking Globally, Acting Locally: Increasing Access to a Wider Range of Diverse Materials."

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)0.019
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.029
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.005
Science and technology studies0.0230.008
Scholarly communication0.0150.017
Open science0.0020.018
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0170.001

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 head0.021
GPT teacher head0.227
Teacher spread0.205 · 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 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
Published2022
Admission routes1
Has abstractyes

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