Writing About Sustainability Science for the Media: How to Be Both True-to-Fact and Tell a Good Story
Bibliographic record
Abstract
Communicating the findings of sustainability science credibly, accurately, and in ways that meet the needs of public communities presents a challenge for academic researchers. This article reviews the findings of communicating sustainability science to a community audience through mainstream media, from an online blog written by a sustainability studies postdoctoral fellow for the New York Times (NYT)–Scientist at Work. The postdoctoral fellow reported in the blog (March 19–26, 2013) on sustainable community development research on the coast of British Columbia. Field reports included textual and photographic information, with supporting multimedia documentation. Based on lessons learned with the NYT–Scientist at Work, this article identifies a set of best practices sustainability scientists might employ to communicate their research both true-to-fact and telling a good story. Recommended communication of sustainability science best practices include: (a) Find the sex, drugs, and rock and roll in the science; (b) It's the scientist's byline—be prepared to defend all the scientific findings regardless of their source; (c) Make science dissemination a part of the research process, not an afterthought; (d) Little of the science will actually get published—find the sound bites; and (e) Leave time for rights and permissions discussions.
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 | 0.073 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.023 | 0.036 |
| Scholarly communication | 0.036 | 0.054 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".