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Record W4296400921 · doi:10.18438/eblip30180

Women of Colour and Black Women Leaders are Underrepresented in Architectural Firms Featured in Key Trade Publications

2022· article· en· W4296400921 on OpenAlexvenueno aff
Nandi Prince

Bibliographic record

VenueEvidence Based Library and Information Practice · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsAuditIndex (typography)Diversity (politics)ArchitectureRepresentation (politics)Key (lock)AccountingFinancial statementSociologyBusinessHistoryComputer scienceLawPolitical scienceWorld Wide Web

Abstract

fetched live from OpenAlex

A Review of: Mathews, E. (2021). Representational belonging in collections: A comparative study of leading trade publications in architecture. Library Resources & Technical Services, 65(3). https://journals.ala.org/index.php/lrts/article/view/7486 Abstract Objective – To measure how well women are reflected, specifically women of colour, in architectural trade publications. Design – Quantitative diversity audit. Setting – Architecture field. Subjects – Architectural firms whose work appeared in four trade publications (Architectural Record, Architectural Review, l’Architecture d’Aujourd’hui, and Detail) in 2019. Methods – A diversity audit was selected to analyze the representation of various subsets of women within the architecture core collections. The Avery index was used to identify architectural firms featured in four trade publications. The quantitative study collected demographic data from 354 firms, featuring 726 women. Within these firms, the author sought to identify women leaders and how many of those were women of colour. The author then used four guiding questions to analyze the journals: (1) individual journals’ coverage; (2) size of the firm; (3) type of firm, and (4) firms which issued a statement in support of the Black Lives Matter Movement and the likelihood of a woman of colour being in a leadership role. Main Results – The key results for the studies guiding questions were: (1) the overall average of women leaders in the firms covered in the journals was 24% and for women of colour 6%. Architectural Record featured the highest proportion of firms with women in leadership roles (28%) and those with women of colour as leaders (9%); (2) women leadership was higher in smaller firms (large 24%; medium 20%; small 31%) as was women of colour in leadership (large 3%; medium 6%; small 9%); (3) insufficient data was found for meaningful analysis of the representation of women according to specialization within the architectural field; and (4) the firms that issued clear BLM statements were highest in the US (15%) overall. Architectural Record, a US publication, featured the highest percentage of firms that made clear BLM statements (27%). Conclusion – The study concluded that there was an underrepresentation of women, women of colour, and Black women in architectural trade publications. The author’s position is that collection development practices should adequately reflect the library users they serve with acquisition actions that increase a more equitable representation. The author stated that the practical implications for this study fall under the rubric of remediation in the following areas: (1) balance inequities in architectural programs by increasing enrollment of women; (2) identify collections which lack inclusivity, balance them with curated electronic resources; and (3) collection policies should reflect readership and encourage a sense of professional belonging. In future studies, the author acknowledges that a qualitative study based on responses from architects would complement the current study.

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)0.005
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.006
Science and technology studies0.0030.003
Scholarly communication0.0050.005
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.069
GPT teacher head0.292
Teacher spread0.224 · 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 designObservational
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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