Discovering Inclusivity in Remote Sensing: Leaving No One Behind
Bibliographic record
Abstract
Innovative and beneficial science stems from diverse teams and authorships that are inclusive of many perspectives. In this paper, we explore the status of inclusivity in remote sensing academic publishing, using an audit of peer-reviewed journal editorial board composition. Our findings demonstrate diversity deficiency in gender and country of residence, limiting the majority of editors to men residing in four countries. We also examine the many challenges underrepresented communities within our field face, such as implicit bias, harsher reviews, and fewer citations. We assert that in the field of remote sensing, the gatekeepers are not representative of the global society and this lack of representation restricts what research is valued and published, and ultimately who becomes successful. We present an action plan to help make the field of remote sensing more diverse and inclusive and urge every individual to consider their role as editor, author, reviewer, or reader. We believe that each of us have a choice to continue to align with a journal/institution/society that is representative of the dynamic state of our field and its people, ensuring that no one is left behind while discovering all the fascinating possibilities in remote sensing.
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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 | 0.207 | 0.469 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.034 | 0.021 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".