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
Makerspaces are common learning spaces providing hands-on opportunities for students to make, create, plan, and play. This chapter describes an equity-oriented (girls) makeathon day. Teacher and teacher candidate participants (n=15) acted as mentors for 22 girls creating wearable technologies, augmenting reality using old t-shirts, and creating a mobile app related to an issue that teen girls face today. The results of this case study focus on results from an adapted questionnaire (teacher efficacy and attitudes toward STEM [T-STEM]) and semi-structured interviews. Qualitative data of this case study research was analyzed through open-coding and triangulated with quantitative data and Mann-Whitney U tests. Participants identified the importance of technologies for their growth as educators and to create safe and supportive environments for girls. Participants highlighted the importance of professional development and support and how to create effective makerspaces. Continued research and opportunities need to be created to encourage diverse educational makeathon events.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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