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.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.029 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.009 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".