Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it