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Record W4407681800 · doi:10.1145/3641555.3705095

SIGCSE's Committee on Expanding the Women-in-Computing Community Fine-Tunes Support and Encouragement

2025· article· en· W4407681800 on OpenAlexaff
Gloria Childress Townsend, Wendy Powley

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicGender and Technology in Education
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

ACM Books recently published Rendering History: The Women of ACM-W. ACM-W is ACM's Women in Computing organization: supporting, celebrating, and advocating for women in computing. Rendering History contains a brief history of ACM-W and an annotated bibliography of important resources concerning women in computing. These two sections sandwich a longer section containing the first-hand stories of 38 women who served ACM-W in its first 30 years. The stories further divide into sections including The Presidents, The Directors, The Chairs, and The Professors and Researchers. This BOF continues the theme of Rendering History. The BOF will divide into subgroups for more intimate conversations about the rewards and challenges of the careers that define our lives and absorb our time. Anticipated groups: Professors, Chairs and Program Directors, and Students. Former Chairs of ACM-W, Valerie Barr and Jodi Tims, will lead a subgroup discussing ACM-W projects that can recruit and retain our students and encourage women in industry. Executive Director and CEO of the Computing Research Association, Tracy Camp, will guide a similar subgroup examining CRA-WP projects (Committee on Widening Participation in Computing Research). The remaining subgroups will examine the issues that attendees bring to the discussion group. MaryAnne Egan and Wendy Powley will oversee a student subgroup; Elizabeth Hawthorne and Rachelle Hippler, a professor subgroup; and Gloria Townsend and Ellen Walker, a chair/director subgroup.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score0.814

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.028
GPT teacher head0.346
Teacher spread0.318 · 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 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
Published2025
Admission routes1
Has abstractyes

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