Social Capital and Persistence in Computer Science of Google’s Computer Science Summer Institute (CSSI) Students
Bibliographic record
Abstract
While a lucrative and growing field, low levels of gender and racial diversity in Computer Science (CS) remain prevalent.Education and workforce support programs with the intention to promote underrepresented students' persistence in CS exist, which teach CS skills, inform of career options, and grow students' network in CS.Studies have demonstrated these programs' effectiveness as it relates to changes in affective outcomes, such as participants' confidence in CS skills and attitudes towards CS jobs.However, the longitudinal impact of CS support programs on participants' build-up of social capital in CS, and the resulting social capital's influence on their persistence in the field, remain unexplored.Motivated by the literature that associates demographic identifiers with access to social capital, and students' access to developmental relationships and career-related resources (social capital) in CS with their persistence, this study explores a CS support program's impact on persistence through capital building.We focus on Google's Computer Science Summer Institute (CSSI), which provided graduating high school students with a 3-week-long introduction to CS.We use interviews with participants who are now 2-5 years out of the program to study CSSI's impact on their social capital and long-term persistence in CS.Thematic analysis reveals three features of the program that influenced students' build-up of social capital, and that the resulting persistence was realized through students' progress towards internships in CS and goals for paying-it-forward in CS.These findings inform our recommendations that future CS support programs and educational settings consider mentorship centered on socioemotional support, opportunities for collaboration, and time for fun social activities.Additional suggestions center on engaging socially-oriented individuals with CS support programs.These insights inform facilitators and educators in CS on design choices that can encourage the persistence of underrepresented students in CS.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".