Bibliographic record
Abstract
This paper discusses how the interactive documentary A Global Guide to the First World War (Francesca Panetta, 2014) effectively integrates interactive technology with historical narratives of the war through a three-way collaboration between news organizations, historians and digital materials from the Imperial War Museum from the perspective of transnational shared history. The paper argues that the different images, videos, news reports and audio clips reflect diverse aspects of common historical themes in the documentary. First, in its seven video chapters, the museum's audiovisual archive covers many countries, and audiences can watch videos related to the WWI alongside historians' commentaries that explain how different nations fought in the war; second, the interactive part is based on an elaborate digital world map, and users can view audio clips, photocopied newspapers and other materials of the war under various web interaction options, and all of the above can be accessed in multiple languages. The natural and direct feeling brought to audiences is the understanding of the WWI experienced by different peoples and countries with a global perspective, instead of the traditional single narrative perspective. Thus, the documentary is a testimony to the shared history of the war. I suggest that the shared historical events and interactive technology make it easier for viewers from different cultural backgrounds to understand this work, and trigger a wide range of historical resonance. Visual media has always led people to leave events in their memories that they did not experience. Although most viewers did not personally experience WWI, historians' commentaries, museums' audiovisual histories and the Guardian's authentic old stories give us credible evidence of the past. The traumatic memories that the images bring to the audiences create an effect of sympathy for the pain of war and the responsibility to fight against it, which transcends racial and national identification. However, this transnational empathy is quite limited. The reason is that the documentary employs an official authoritative narrative strategy rather than the perspective of the individual. It somehow creates a sense of alienation for the viewers, who realize that these are collages of archival records rather than a kind of historical memory closely related to themselves.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".