Bibliographic record
Abstract
Who is the enemy in the post-9/11 period? Do we as a society build the enemy's identity, and if so how? This thesis explores the types of discourse, including binary opposition, and practices, including labelling and profiling, used by the media in the building of enemy identities. Using a qualitative research approach, I analyze over ninety articles from The Province, a Canadian newspaper, to investigate how one print media presented enemies in the thirty days following the World Trade Centre bombing on September 11, 2001. After situating my analysis within two overlapping theoretical perspectives, the critical discourse and the social constructionist perspectives, I demonstrate that the label of "enemy" is applied by society and the state to an entity that appears to pose a threat---I underline that appearing "different" can qualify a person or group for arbitrary surveillance among other human rights violations. My research will show that the issue of ethnicity is crucial to understanding how enemies are constructed. Prejudicial attitudes have the potential to influence our politicians, seep into our immigration systems, and affect our policies and criminal laws.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.029 | 0.054 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".