Supporting teachers in relational pedagogy and social emotional education : a qualitative exploration
Bibliographic record
Abstract
We examined the beliefs and experiences of three elementary school teachers who, over \none school year, participated in bi weekly, guided discussions of attachment and care \ntheories that introduced them to relational pedagogy as a way of supporting students‟ \npositive social, emotional, and academic growth. Teachers‟ beliefs about the aims of \neducation were assessed at the beginning and end of the study and for the duration of the \nstudy they each kept a journal to document and reflect on their classroom interactions. \nFindings revealed teachers‟ understandings of the aims of education reflected a more \nrelational perspective at the end of the study than the beginning. Seven themes emerged \nfrom the journals capturing the teachers‟ commitment to fostering caring relationships in \ntheir classrooms; their hesitancy to fully implement relational pedagogy as well as \nmissed opportunities to do so; the frustration they experienced leading to abandoning \nrelational pedagogy; awareness of their “mistakes”; their feelings of isolation as they \nrealized relational pedagogy required a supportive school environment and their \nsuccesses. Implications for pre- and in-service teacher education are 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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".