Teachers’ shifting metaphors of practice
Bibliographic record
Abstract
Using teacher-led research practices during a unique set of teaching circumstances, this paper offers insight into teachers’ shifting and evolving pedagogies, demonstrating through shifting metaphors, and elicited through arts-based research. This study investigated pandemic classroom site pivots and provides a robust and multimodal framework for understanding elementary teachers’ evolving pedagogies. This case study research is situated within an inquiry-based action research cycle and highlights the impact of pandemic conditions on teachers’ practices. including insight and guidance for pedagogical change over a range of sociopolitical and systemic shifts in teaching contexts. Our study focused on how our teacher-led inquiry, framed as a community of practice, built teachers’ voices and reflective practices. Our research group used both teacher-created metaphors and arts-based practices as a lens into teachers’ shifting pedagogies and practices. Developing pedagogies and teacher identities were articulated through processes whereby teachers learned together while teaching under pandemic restrictions, and to nimbly shift from online to in-person teaching and back again, discovering how their evolving identities enhanced the diversity of children’s voices in the classroom.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.046 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| 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".