A Poliheuristic Reading of Securitization Theory: A Study of Media’s Role in Shaping the Decision Environment Regarding Refugees in Britain and Canada
Bibliographic record
Abstract
This thesis studies media frames as reflections of decision making environments. This\nhas especially been examined in relation to the decision making environment that\nexisted in Canada and the United Kingdom in relation to the resettlement of Syrian\nrefugees in the time frame between January 2009 and December 2016.\nTo identify the decision making environments, the content of the population of audio visual news pieces (1115 pieces) aired by Global News, CBC, Sky News and BBC\nabout Syrian refugees were coded by ’Atlas.ti’. Five generic frames were used to\ndetermine the nature of the content and a number of sub-frames were used to identify\nthe direction of the frames. As a supplementary step, the general patterns identified in\nthe examined news networks were classified and an insight into the decision making\nenvironments was provided on this basis. This was done with a special attention to\nidentifying possible securitizing patterns in the media. The identified environments\nwere then examined in the context of the poliheuristic model of decision making.\nBy demonstrating the predictive capacity of studying media frames and framing\npatterns, the thesis suggests that the media expose their audience to a dominant\ndecision making environment in relation to an issue of concern and stabilize that\ndecision making environment by repeatedly exposing their audience to the same\nframing patterns.\nKeywords: decision-making environments, foreign policy, media frames, poliheuristic\nmodel, refugees, securitization theory
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".