The Memorial Afterlives of Online Crowdsourcing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
From May 2014 to March 2019 the Imperial War Museums launched a large-scale digital crowdsourcing project, ‘Lives of the First World War’. ‘Lives’ melded official and unofficial datasets to create an integrated database of people who had participated in the First World War. Over the course of the project 7.7 million individual histories were collected. After the initial collection phase, ‘Lives’ became a permanent digital memorial and database. This article investigates how ‘Lives’ contributed to public understandings of the First World War during and after its centenary. While undoubtedly an impressive and difficult undertaking, this article suggests that large scale data collection as a methodology on its own will replicate collection biases, unless married with specific collection drives. In the case of the First World War, this means that global majority narratives are subsumed by white British ones, at the expense of historically realistic data. The skewed datasets that come from large crowdsourced projects have widespread implications for cultural memories of events if they are to be digitally preserved within national collections.
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.
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 | 0.001 | 0.002 |
| 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) | 0.003 | 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 it