Exploring the Arctic: An Awareness Experiment in Science Journalism and Personal Narrative
Bibliographic record
Abstract
Journalism coverage of the Canadian Arctic is limited and often inconsequential, or inaccessible to the broader public due to highly specialized content, e.g. information locked in scientific papers. This is despite the fact that the Arctic is of national as well as global importance. This discrepancy may be attributed to a general deficit of journalism coverage of climate issues, which are closely linked to the Arctic region. Furthermore, as a remote and unique location, an “out of sight, out of mind” mentality both physically and conceptually removes the region from public awareness. Very few non-Arctic residents are able to experience the region first-hand, and the true vividness of the area is often lost in traditional scientific publications. However, innovations in digital storytelling and narrative could open the Arctic to increased awareness, thereby bringing climate and polar science to the forefront of tomorrow’s journalism. This Research-Creation Project combined in-person experiences on a scientific Arctic cruise with traditional reporting methods to create a catalogue of innovative multimedia pieces in a dedicated online Story Hub. Inspired by the works of Robin Wall Kimmerer and the ideas of Randy Olson, the project aimed to increase the awareness of the region with approachable and engaging narratives, sharing knowledge and personal observations through storytelling. Designed to foster passion and interest, not scientific expertise, the Research-Creation Project is a blueprint for interweaving scientific journalism with personal narrative reporting as a stepping stone to more in-depth science communication.
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 | 0.014 | 0.039 |
| 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 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".