What We Say vs. What They Do
Bibliographic record
Abstract
In attempts to broaden participation in computing, the computer science education community has developed a wide variety of outreach activities to encourage students of different ages to learn computational thinking techniques and to develop an interest in computer science. In their recent surveys of the CSed literature, Decker, McGill, and Settle identify over eighty papers on K-12 outreach activities, of which approximately forty address middle-school coding camps. However, summer coding camps are offered by a much wider variety of organizations than computer science educators committed to diversifying the field. Some are offered by organizations committed to diversity, such as Black Girls Code and Girls Who Code. Others are offered by universities for recruitment, and necessarily to support diversification. Still others are offered by for-profit entities. What are the relationships between the two models of camp? Do the ideas that appear in the research literature filter out to the more mainstream camps, or do the more mainstream camps provide a very different model of computer science? In this project, we reviewed both the computer science education literature (52 sources representing 45 camps) and summer code camps identified on the World-Wide Web (480 different camps). In this poster, we report on common approaches and themes that others may choose to adapt or adopt. We also explore significant differences between the research-centered camps and the mainstream camps in approach, language, and apparent outreach goals.
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 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 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".