Bibliographic record
Abstract
This bachelor thesis examines the media debate over the possible construction of Keystone XL pipeline. In 2008 the Canadian company TransCanada submitted an application for presidential permit to construct the pipeline. Such permit was needed as the pipeline would be crossing the U.S. international border. In 2015 President Obama decided not to issue the permit. This thesis examines how was this topic debated in the two prestigious US newspapers The Washington Post and The New York Times. The research methodology that was chosen for this paper is content analysis. Unit of content is an article from abovementioned newspapers published between 2008 and 2015 that had the headword "Keystone XL" in its headline. The thesis relies on framing theory. It aims to identify the frames that dominated the debate and their main storylines. In its first part the paper defines the methodology and its theoretical framework; the second part examines the results of the content analysis and afterwards deals in depth with each of the frames. The three dominant frames were "environmental frame", "energy security frame" and "economic frame". Each frame is put into the broader context of the academic research dealing with the same topic.
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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".