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Record W4379645288 · doi:10.1109/msp.2023.3236475

The Evolution of Women in Signal Processing and Science, Technology, Engineering, and Mathematics

2023· article· en· W4379645288 on OpenAlexaff
Rabab Ward

Bibliographic record

VenueIEEE Signal Processing Magazine · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSignal processingComputer scienceScience and engineeringMathematics educationTheoretical computer scienceMathematicsDigital signal processingEngineeringEngineering ethicsComputer hardware

Abstract

fetched live from OpenAlex

When I began writing this 75th anniversary article celebrating women in signal processing (SP), I reread the 1998 editorial titled “Fifty Years of Signal Processing: 1948–1998”[1]. At that time, IEEE had more than 300,000 members in 150 nations, the world’s largest professional technical Society. Within the IEEE umbrella, there were 37 IEEE Societies and technical groups, and the IEEE Signal Processing Society (SPS) was the oldest among its many Societies.

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)0.009
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0080.018
Scholarly communication0.0110.011
Open science0.0010.006
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0090.002

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 head0.014
GPT teacher head0.249
Teacher spread0.235 · 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.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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