Writing for the world: Enhancing engagement and connection with an international audience
Bibliographic record
Abstract
Writing for the world: enhancing engagement and connection with an international audience.How we see and seek to understand the world is situated in a learnt worldview.Culture, heritage, and knowledge systems influence the research questions asked, how they are answered, and how findings are interpreted.Connecting with an international audience does not mean orientating to Western sensibilities, nor following Western centric conversations.Rather, work can be firmly rooted in its relevant cultural relevance, but also contextualised for a wider readership.Some studies will have implications of international relevance, where careful consideration about transferability is needed.The importance of other studies may be precisely because they open an area of thinking where transferability may not be expected for genuinely problematic issues of relevance.The purpose of this editorial is to explain, from an editorial perspective, what we seek when assessing papers submitted to Palliative Medicine.The journal has a highly international readership, and we want to publish papers that connect meaningfully with that audience.We set out here six aspects that we consider important when you are planning, writing, and submitting papers that enable international engagement with your work.
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.
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 | 0.017 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".