Critical care for the early web: ethical digital methods for archived youth data
Bibliographic record
Abstract
Purpose This paper aims to provide a brief overview of the ethical challenges facing researchers engaging with web archival materials and demonstrates a framework and method for conducting research with historical web data created by young people. Design/methodology/approach This paper’s methodology is informed by the conceptual framing of data materials in research on the “right to be forgotten” (Crossen-White, 2015; GDPR, 2018; Tsesis, 2014), data afterlives (Agostinho, 2019; Stevenson and Gehl, 2019; Sutherland, 2017), indigenous data sovereignty and governance (Wemigwans, 2018) and feminist ethics of care (Ciforet al., 2019; Cowan, 2020; Franzkeet al., 2020; Luka and Millette, 2018). It demonstrates a new method called an archive promenade, which builds on the walkthrough and scroll-back methods (Lightet al., 2018; Robards and Lincoln, 2017). Findings The archive promenades demonstrate how individual attachments to digital traces vary and are often unpredictable, which necessitates further steps to ensure that privacy and data sovereignty are maintained through research with web archives. Originality/value This paper demonstrates how the archive promenade methodological intervention can lead to better practices of care with sensitive web materials and brings together previous work on ethical fabrications (Markham, 2012), speculation (Luka and Millette, 2018) and thick context (Marzulloet al., 2018), to yield new insights for research on the experiences of growing up online.
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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 | 0.144 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.043 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".