"Kyl joka mimmist voi tulla kui hyvä vaan" : dialoginen analyysi sukupuolisuudesta skeittaamaan oppimisessa
Bibliographic record
Abstract
This qualitative case study analyzes how female skateboarders are seen as learners in social communities of practice. Previous studies have shown that skateboarding communities are dominantly masculine. Skateboarding is also learned in these communities of practice with and from other skateboarders. This study examines with dialogical analysis how female and male skateboarders negotiate their participation in these communities of practice. Previous studies have shown that females actively negotiate their right to participate in the skateboarding communities. Female skateboarders have also started to organize skateboarding events for females only. This study examines if the gendered tensions are reflected in the dialogues between skateboarders. Dialogical analysis of multivoicedness was used in this study. The data was collected with focus group discussions. Two female and two male skateboarders discussed aspects of learning and gender in two separate sessions. The duration of both sessions was 30 minutes. The data was transcribed and analyzed with a dialogical analysis of multivoicedness. Also content analysis was performed in order to find social representations of female skateboarding and learning. The connection between learning and community is very evident in the "I-positions" and "inner Others" that were found in the data. The skateboarders emphasize the collective and inclusive nature of skateboarding communities and see that females have the same possibilities to learn in these communities as males. Female voices stand out or negotiate with male voices only in rare situations. Social representations of "how females learn how to skateboard" reflect previous studies from United States, Canada, and Sweden. Social representations perceive female skateboarders as few in number, lazy to practice, just in it because it is trendy and afraid of physical risk taking.
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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 | 0.003 | 0.004 |
| 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 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".