Bibliographic record
Abstract
The aim of the bachelor thesis is to explore the impact of securitization and securitization discourse on the process of transformation of the approach towards the borders, their function and towards their importance. Terrorist attacks from September 11. dramatically changed the US-Canada border as well as the whole cross-border dynamics between both states. From undefended border the Canada-Unites States border became heavily fortified and it underwent an increase of border patrols, surveillance techniques and various security measures. This change was possible because of the creation of securitization discourse and the purpose of this thesis is the analyse its creation and development and to examine the progress of the securitization itself. The thesis is based on the theory of securitization which was created by the authors of Copenhagen School, but because of the fact that the original securitization theory became the subject to criticism, the theoretical framework of the thesis is provided by Thierry Balzacq, who put an emphasis on the context and on the complexity of the analysis. Discursive analysis takes place between the September 11. attacks and the end of the year 2005 and is focused on policy statements, political decisions and relevant speech acts. Second part of the thesis addresses...
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".