Reaching revelatory places: the role of solicited diaries in extending research on emotional geographies into the unfamiliar
Bibliographic record
Abstract
Solicited diaries can be used to delve into otherwise unreachable interpretations of social and physical experiences. Diaries help researchers to understand the embodied and the emotional in human geography. In this paper we develop on the work of multiple disciplines, enhancing the rationale as to why and how to employ diaries, and highlighting the benefits and drawbacks associated with this methodological tool. Notably, we extend the literature related to solicited diaries into the unfamiliar through examples from our research with scientists working in Antarctica who maintained diaries for us. We illustrate the potential for such diaries to elicit meaningful narratives that complement and extend data collected through interviews. Diaries provide timely, in situ space for emotional reactions to and contemplations of the immediate environment, as well as on every day and out of the ordinary events, while interviews provide interviewees with time and distance from the field to offer reflections based on lasting impressions. In particular, when combined, solicited diaries and interviews can substantially enrich investigations of those innately human, yet often elusive, places of the mind – revelatory places – in‐between people and the environments that move them.
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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 | 0.077 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".