Echo or organic: framing the 2014 Sochi Games
Bibliographic record
Abstract
Purpose The purpose of this paper is to perform a comparative analysis of how traditional media and social media framed the 2014 Sochi Winter Olympic Games. Design/methodology/approach The researchers examined newspaper articles pertaining to the Sochi Olympics and Tweets containing #SochiProblems to determine if differences or overlap existed in terms of themes and frames. A thematic analysis was conducted with the qualitative software Leximancer. Findings An analysis of 2,856 newspaper articles and 497,743 Tweets revealed three frames across the two media platforms including: the setting, the politics, and the games. There was both a divergence and convergence of content. While there was an echo chamber in terms of discussions regarding political controversies, organic content related to conditions and accommodations existed primarily on Twitter. Originality/value This study sought to investigate whether organic content on Twitter could withstand the transference of sentiments that emerge in traditional media. This study adds to the current body of the literature by examining whether there is a convergence or divergence of content across media platforms pertaining to an international sporting event.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".