Bibliographic record
Abstract
The current study is a two-year case study focusing on an upper-elementary girl who had been diagnosed with selective mutism in 1st grade. While multiple theoretical frameworks have been used to explain selective mutism, the current study borrowed the frameworks of critical sociocultural theory, a social semiotic theory of multimodality, and self-efficacy theory. The data consisted of daily field notes written by the researcher, video and audio recordings, artifacts of schoolwork and student written communication. The researcher served as Tina’s learning specialist in Year 1 and her homeroom teacher in Year 2 during the data collection period. Those data sources were used to explain the evolution of new insights, identities, and pedagogical practices designed to support Tina in the school setting. The findings showed that being attentive and observant, establishing a safe learning environment, and cultivating a strong teacher-student relationship were critical to student success. In addition, the role of writing as a communication tool and use of digital tools supported Tina’s growth. In conclusion, implications for teacher development will be discussed.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".