SIGCSE's Committee on Expanding the Women-in-Computing Community Fine-Tunes Support and Encouragement
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.105 | 0.061 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".