The Supermodel Astronaut Challenge: traversing frames of mind
Bibliographic record
Abstract
Purpose The Supermodel Astronaut (SMA) Challenge began with a group of women in a graduate class who joined together to take the pledge “I Am Enough.” The goals of the pledge are to practice positive affirmative actions of self-acceptance, self-grace, self-improvement and positive encouragement of oneself and others. The paper aims to discuss this issue. Design/methodology/approach The SMA Challenge involves an online video pledge to encourage women and girls to demonstrate their opposition to the promotion of singular ideals of body perpetuated through media. Various individuals and groups have created music videos titled SMA to the soundtrack created by Ellen Tift (the originator of the project). Findings Here, framed by Daignault’s (1983) theories on curriculum construction, the authors critically reflect on their support of the idea of the video, but also their apprehension and insecurities in participating in the video production. Originality/value From reflections, writings and dialogic discussions, they determined five embodied frames of mind that supported them in traversing the liminal space of new learning: imagining the possible, learning in doing, settling in vulnerability, journeying through empowerment and heightening self-reflection.
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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.047 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".