Bibliographic record
Abstract
All Rights Reserved. All material appearing on the this article and the idea (“content”) is protected by copyright under U.S. Copyright laws/ Canadian Copyright/ EU Copyright laws/ Berne Convention 1988 and is the property of authors or the party credited as the provider of the content. You may not copy, reproduce, distribute, publish, display, perform, modify, create derivative works, transmit, or in any way exploit any such content, nor may you distribute any part of this content over any network, including a local area network, sell or offer it for sale, or use such content to construct any kind of database. You may not alter or remove any copyright or other notice from copies of the content on this article. Copying or storing any content except as provided above is expressly prohibited without prior written permission of the author or the copyright holder identified in the individual content’s copyright notice. For permission to use the content, please contact the authors. Under Canadian Crown Copyright License: Copyright is an author/creator's exclusive legal right to reproduce, publish and sell a work. Abstract. The use of cutting-edge technologies has revolutionized the way political marketing strategies are formulated. By optimizing the political environment through advanced tools, it is now possible to create effective and inclusive strategies that cater to the needs of diverse groups. With the help of new technologies, it is possible to analyze vast amounts of data available on the internet and compile it based on various factors such as time stamp, gender, age, and orientation. This information can be used to generate insights into the preferences of different groups, which can help in creating targeted political marketing strategies. In essence, the use of new technologies in political marketing has made it possible to create data-driven and inclusive strategies that cater to the needs of diverse groups. By leveraging these technologies, politicians can connect with their constituents in a more meaningful way and create a positive impact on the political landscape.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.042 | 0.018 |
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; both teacher heads agree on what is shown here.
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".