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Record W4403036088 · doi:10.1177/10935266241281786

Maude Abbott: “A Feminine Misfit in an Exclusive Male Environment” and Her Strategies for Success

2024· article· en· W4403036088 on OpenAlexafffundabout
James R. Wright

Bibliographic record

VenuePediatric and Developmental Pathology · 2024
Typearticle
Languageen
FieldMedicine
TopicHistory of Medical Practice
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersMcGill University
KeywordsReputationMedicineManagementFamily medicineLawPolitical science

Abstract

fetched live from OpenAlex

Maude Abbott was a pioneering female Canadian physician who became a world authority on medical museums and congenital heart disease. Abbott spent almost all her career in highly sexist, discriminatory work environments. This paper reviews Abbott's life and accomplishments, but, more importantly, analyzes her pathway to success in the masculine world of early 20th-century academic pathology. Abbott, though well-trained as a pathologist, never provided clinical service, but instead worked as museum curator at McGill University. She established the International Association of Medical Museums (predecessor to the International Academy of Pathology), edited its journal, and essentially ran the organization. Abbott, surrounded by influential males, dealt differently with each. In general, she recognized that male doctors believed women lacked the gravitas to lead major initiatives but that she could circumnavigate this supposed impediment by co-leading projects with male counterparts, preferably ones too busy to get in her way. She repeatedly used this approach, and by doing most of the work but sharing credit, succeeded in gaining reputation, accomplishment, and advancement. Abbott's pioneering work on congenital heart disease established her as one of the founders of pediatric pathology, and, overall, her career promoted the entry of women physicians into the pathology profession.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.020
GPT teacher head0.290
Teacher spread0.270 · 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 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
Published2024
Admission routes3
Has abstractyes

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