Comment on egusphere-2023-3121
Bibliographic record
Abstract
Abstract. Science communication is an important part of research, including in the geosciences, as it can benefit society, science, and make science more publicly accountable. However, much of this work takes place in “shadowlands” that are neither fully seen nor understood. These shadowlands are spaces, aspects, and practices of science communication which are not clearly defined and may be harmful with respect to the science being communicated or for the science communicators themselves. With the increasing expectation in academia that researchers should participate in science communication, there is a need to address some of the major issues that lurk in these shadowlands. Here the editorial team of Geoscience Communication seeks to shine a light on the shadowlands of geoscience communication and suggest some solutions and examples of effective practice. The issues broadly fall under three categories: 1) harmful or unclear objectives; 2) poor quality and lack of rigor; and 3) exploitation of science communicators working within academia. Ameliorating these will require: 1) clarifying objectives and audiences; 2) adequately training science communicators; and 3) giving science communication equivalent recognition to other professional activities. By shining a light on the shadowlands of science communication in academia and proposing potential remedies, our aim is to cultivate a more transparent and responsible landscape for geoscience communication—a transformation that will ultimately benefit the progress of science, the welfare of scientists, and more broadly society at large.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.030 | 0.016 |
| Insufficient payload (model declined to judge) | 0.174 | 0.109 |
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".