Gendered Language in Children's Coding Program Websites
Bibliographic record
Abstract
This natural language processing (NLP) content analysis investigates if advertising language on children's coding program websites may be discouraging girls from engaging in STEM activities, and thus contributing to the STEM gender gap. Specifically, we seek to examine the prevalence of communal and agentic messages used by these websites, due to the both their gender associations and connection with STEM fields. We predict that the websites will contain predominantly agentic messages, which may signal to girls that the program does not fit with their interests and goals, thus contributing to girls' disengagement with STEM activities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.024 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.019 | 0.002 |
| Open science | 0.021 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.047 | 0.237 |
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; both teacher heads agree on what is shown here.
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".