Modelling the Temporal Effect of Terrorism on Tourism in Kenya
Why this work is in the frame
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Bibliographic record
Abstract
<p>Terrorist attacks have escalated over the recent years in Kenya, with adverse effects on the tourism industry. This study aims to establish if a long-run equilibrium exists between terrorism and tourism in Kenya between the years 1994 and 2014. To reinforce the robustness of the results, both Autoregressive Distributed Lag (ARDL) bounds testing and the Vector Error Correction Model (VECM) techniques are used to investigate the problem. A Granger causality test is also carried out to ascertain the direction of the relationship if one exists. The evidence from ARDL and the VECM testing procedure suggest that there is no long-run equilibrium between terrorism and tourism in Kenya. Terrorism does not Granger cause tourism and vice versa. However, short-run effect indicates that terrorism negatively and significantly affects tourism.</p>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it