Using online comments to explore public reaction to the oil sands monitoring plan announcement: an argumentation analysis
Bibliographic record
Abstract
The oil sands have proven to be a controversial topic for Canadians. Whether the oil sands are a detriment to the environment or a benefit to the economy, Canadians must decide for themselves where they stand on this issue. One contributing factor to the understanding of various members of public communities is the information and analysis available in the institutional media. Because the media audience, referred to as “the public” in this paper (with the understanding that this refers to diverse communities of readers and audience members), relies at least in part upon the media to gather and disseminate information, it is imperative to see how they understand this data. This study was undertaken to explore public understanding of the oil sands based upon a related set of institutional media announcements. It is important to study public understanding of media because it can reveal the intricacies of one source of information and viewpoints that contribute to public discussion and understanding. This study may also be useful to inform science educators’ efforts to prepare students to engage with socioscientific issues. This may be used by media as another way of presenting information that the public would value.
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 | 0.016 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".