Bibliographic record
Abstract
The result of the United Kingdom European Union membership referendum (henceforth the Brexit referendum) was historic as it signified the beginning of the United Kingdom’s exit from the European Union. During the referendum campaign, newspapers played a key role in disseminating information and potentially influencing what topics were deemed more important in the public eye. This paper examines the portrayal of both the economy and immigration in the press before and during the Brexit referendum. Used as a data source for this examination is the Daily Mail, one of the most widely distributed newspapers in the United Kingdom both in print and online. The author undertook a media content analysis on over 40 articles published by the Daily Mail between April 2016 and June 2016 to discern patterns in coverage. This study seeks to offer insights as to how the topic of immigration surpassed that of economics as the most salient topic during the referendum due to agenda setting and media framing by the likes of the Daily Mail. While this paper speculates that these measures may have affected the outcome of the referendum, further data and investigation would be required to warrant such a conclusion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".