“Dear Diary” revisited: reflecting on collaborative journaling
Bibliographic record
Abstract
The genesis of this article was a request from the Journal of Geography in Higher Education to provide a reflection piece about our article ‘Dear Diary: Early Career Geographers Collectively Reflect on their Qualitative Field Research Experiences’ (2011) that won the journal's biennial award for 2009–2011. This request has afforded us the opportunity to reconnect as a team and, through self-directed interviews, to reflect upon how writing ‘Dear Diary’ continues to influences our current perceptions of journaling in qualitative research. More specifically, we focus here on the relationships between journaling and our approach to research, team-based collaboration, and our current teaching and mentoring practices. We all continue to keep fieldwork journals and perceive reflexive journaling as a crucial tool for qualitative methods and other collaborative ventures.
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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 | 0.066 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.022 | 0.028 |
| Scholarly communication | 0.026 | 0.027 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.007 | 0.011 |
| 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".