COMMUNICATING POSITIVE ACTIONS ABOUT CLIMATE CHANGE IN FRENCH CANADA: EXPERIMENTING AND EVALUATING AN INNOVATIVE WEB MEDIA
Bibliographic record
Abstract
This study is part of a larger research project related to the recent creation of the first French Canadian Web media dedicated to promoting action to fight climate change (CC). This media, called ?unpointcinq? (onepointfive), primarily targets adults of the French province of Quebec who are generally interested in the subject of CC, but find it either too complex or far from their personal concerns. It disseminates various contents about the province?s diverse initiatives ? individual, community-based, governmental, etc. ? regarding the fight against CC. The originality of this media relates to its positive tone. Information about CC often presents fear-inducing messages that stress its negative consequences, using an apocalyptic rhetoric (Benjamin et al., 2016; Spence & Pidgeon, 2010). However, research in environmental psychology and environmental communication show that negative framing can be ineffective for engaging and mobilizing publics around CC issues (Jang, 2013; Cox & Depoe, 2013), encouraging researchers and practitioners to test other communication strategies, including different types of positive frames. This mixed-methods research is divided in two parts. First, before the Web media was launched, we conducted a segmentation study using a quantitative questionnaire sent to 1200 Quebeckers, assessing attitudes and behaviours regarding CC, but also perceived relevance and interest towards different themes and frames potentially covered by ?unpointcinq?. This study allowed us to identify priority segments to target, as well as the most efficient frames to use in the media. In the second qualitative phase, we conducted five semi-directed focus groups within the first four months of the media being launched. A total of 40 participants from the segments most receptive to the media evaluated its first contents. These interviews were conducted in the spirit of a living lab and in an iterative manner, where participants and researchers both made recommendations to the editorial and production team to better frame the information. In this present conference, we propose to present the most salient results of the two phases of this ongoing research project.
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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".