Geek As a Constructed Identity and a Crucial Component of Stem Persistence
Bibliographic record
Abstract
The fields of science, technology, engineering and mathematics (STEM) have long been the bastions of the white male elite. Recently, academia has begun to recognize gender and ethnic disparities. In an effort to expand the recruitment pool for these STEM fields in college, various efforts have been employed nationally at the secondary level. In California, the latest of these efforts is referred to as Linked Learning, a pedagogy that combines college preparation with career preparation. The current study is investigating the connection between what has been referred to in current scholarship as "Geeking Out" with higher academic performance. The phenomenon of “Geeking Out” includes a variety of non-school related activities that range from participating in robotics competitions to a simple game of Dungeons & Dragons. The current project investigates the relationship between long term success in STEM fields and current informal behaviors of secondary students. This particular circumstance where Linked Learning happens to combine with "Geeking Out" is successful due to the associated inclusionary environment. Methods included a yearlong ethnographic study of the Center for Advanced Research and Technology, a Central Valley school with a diverse student body. Through participant observation and interviews, the main goal of this research is to examine the circumstances that influence the effectiveness found in the environment of the Center for Advanced Research and Technology.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".