The Effect of the CCERS STEM + C Project on Information Technology Efficacy in Terms of Gender and Grade Level
Bibliographic record
Abstract
Information technology has become ubiquitous in the 21st Century. Acquiring the skills and confidence to navigate the computational arena is all but obligatory for educational and professional success. Underrepresentation of women in the wide variety of fields associated with information technology is an authentic concern for both the individual and society as a whole. Various studies have emphasized the importance of stronger representative of marginalized groups to bolster creative thinking and a variety of perspectives. The CCERS STEM + C Program is a long-term hands-on environmental restoration project that has been embedded in the New York City Department of Education public schools. Students work to restore the native oyster population to New York Harbor through both field work and working with large sets of data on the open-access platform. One of the several areas studied throughout this program is the motivation and self-efficacy of the students, especially students who are underrepresented in the STEM and technology fields. Student surveys were initiated by 764 students with 513 participants and non-participants completing the survey. Aimed at eliciting levels of several self-reported factors, the survey included a subscale measuring levels of confidence in technological abilities. Results of the survey indicated that 9th grade female students have a higher level of self-efficacy and motivation than female students in the later high school grade levels. These results are consistent with the waning motivation and interest of female students in technology and STEM found in other studies.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".