Serendipity, Synchronism and Self-Study: Looking Back at Rural Identity Research and Herstmonceux Castle to Move Ahead
Bibliographic record
Abstract
I came to self-study and Herstmonceux Castle as a researcher from the Canadian prairies who was keenly interested in rurality, rural identity, teacher/teacher educator identity, and the complex ways that such socially constructed concepts interact. What I found as a new self-study researcher and first-time attendee at the Castle Conference, was a serendipitous journey that profoundly and synchronously influenced my identity, practice, and research. The study I conducted, which looked at the impacts of rural experience and ways of knowing, doing, and being on the identities and practices of teachers and teacher educators, allowed me to explore the notion/concept of rural capital and the many ways it is enacted and utilized in educational settings. Through the analysis of multiple data sources, including my master’s and doctoral theses, a video created in my doctoral program in 2010, and five recorded Zoom conversations with educators who also had rural backgrounds/experience(s), four main categories or themes related to the enactment of rural identity in practice emerged: advocacy and commitment, relationality and connection, curriculum and pedagogy, and leadership and service. Together, these themes highlight the complex and multifaceted ways rural experience impacts not only the identities of rural educators but also their actions inside and outside the classroom. In addition to profound impacts on my own identity, practice, and research, such understandings have much to offer the field of rural education in terms of teacher preparation, recruitment and retention, and the sustainability of rural education and communities.
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.040 | 0.063 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.009 |
| 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".