“Home sweet home?” Struggles of intracultural “betweenness” of doctoral fieldwork in my home country of Jamaica
Bibliographic record
Abstract
The culture of silence surrounding the nuanced challenges of fieldwork is being broken but there is still a long way to go. In the last two decades or so, an interesting discourse has developed on experiences of “betweenness” in the field. However, a glaring issue is the underrepresentation of the experiences of Global South researchers and doctoral students returning to their homelands to conduct fieldwork. Much has been written about cross‐cultural fieldwork and its associated challenges, yet the “betweenness” experienced during intracultural research on the impacts of large‐scale tourism is scantly studied. This paper begins with an overview of producing knowledge through qualitative means, followed by an account of the insider/outsider debate that informs this paper. It sheds light on the intracultural dimensions of “betweenness,” which are framed within my experience of being an outsider and an insider during my return to my home country, Jamaica, for doctoral fieldwork. The nuanced challenges and negotiations that came with my “betweenness,” concerning phenomena such as “gazing back,” are at the centre of my discussion. My conclusion is that “betweenness” is not limited to those conducting cross‐cultural studies but also affects those conducting intracultural studies. Therefore, researchers returning home for fieldwork should be on their guard for the implications of “betweenness” for the success of the research process.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.035 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".