A labour of love: Cross‐cultural research collaboration between Australia and Indonesia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Novel combinations of global conditions, issues under investigation and research alliances require constant reassessment of how to conduct cross‐cultural research. Here we recount an exploratory investigation considering cross‐cultural research between Australian and Indonesian researchers. This paper sets out a range of considerations for practitioners of cross‐cultural research between our two countries. This investigation supports intentions to develop trans‐disciplinary climate change adaptation research but is applicable across multiple research topics and disciplines. We engaged a small multi‐disciplinary mix of researchers, from both countries, conducted two initial focus groups, and subsequently involved participants in drafting of this paper as an exploration of how being cross cultural could manifest. We highlight that cross‐cultural collaborations occur in environments of both cultural differences and power differences. Four main strategies emerged for dealing with the challenges (or opportunities): working respectfully, being reflective of cross‐cultural research practice, being flexible, and learning about culture. Overarching these strategies, we found cross‐cultural research requires considerable extra (long term) effort to tackle and that this is sustained by researchers' intrinsic motives to care for people and place, making this type of research a distinctive labour of love. Finally, we found similarities between cross‐cultural research and climate change adaptation research (even when conducted within one country) where both endeavours call for boundaries of places, cultures and disciplines to be crossed in order to effectively engage with complex topics and environments. Negotiating the liminalities here often defies set formulas and requires a willingness to engage with and ‘muddle through’ the messiness. Our findings will be of value to those undertaking cross‐cultural research across a wide range of issues.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it