Data Humanism: Examining how the British newspaper, The Guardian, depicted the British Mad Cow Disease Crisis from 1986–1996
Bibliographic record
Abstract
This research examines the use of data visualization not only to inform audiences about a particular event but mainly to humanize data and uncover the missing content and hidden stories. This research uses digital storytelling as an engaging tool to represent how the British newspaper, The Guardian, depicted the British Mad Cow Disease Crisis from 1986 to 1996. By incorporating a set of recent theories, such as thick data, local data and feminist data visualization, this research emphasizes the need to contextualize data so that these theories can help to fill the context-loss gap generated in standard data analysis. Employing the mixed methodologies of practice-based research, mapping as research, and research through design, this thesis comprises: 1) a written document stating the research process; 2) a series of iterations leading to an interactive web-based data story. The final storytelling aims to answer the questions of how we might represent the British Mad Cow Disease Crisis from 1986 to 1996 using a digital technique to approach different understandings of this historical event and how digital technologies and media benefit users and evoke their emotions.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".