CRITICAL CARE & THE EARLY WEB: ETHICAL DIGITAL METHODS FOR ARCHIVED YOUTH DATA
Bibliographic record
Abstract
This paper demonstrates an ethico-methodological approach to researching archived web pages created by young people throughout 1994-2005 that was collected and stored by the Internet Archive. Rather than deploying a range of computational tools available for collecting web data in the Internet Archive, my approach to this material has been to start with the person: I recruited participants through social media who remembered creating websites or participating in web communities when they were younger and were interested in attempting to relocate their digital traces. In a series of qualitative, online semi-structured interviews, I guided participants through the Wayback Machine’s interface and directed them towards where their materials might be stored. I adapted this approach from the walkthrough method, where I position the participant as co-investigator and analyst of web archival material, enabling simultaneous discovery, memory, interpretation and investigation. Together, we walk through the abandoned sites and ruins of a once-vibrant online community as they reflect and remember the early web. This approach responds to significant ethical gaps in web archival research and engages with feminist ethics of care (Luka & Millette, 2018) inspired by conceptual framing of data materials in research on the "right to be forgotten” (Crossen-White, 2015; GDPR, 2018; Tsesis, 2014), digital afterlives (Sutherland, 2020), indigenous data sovereignty and governance (Wemigwans, 2018), and the Feminist Data Manifest-No (Cifor et al, 2019). This method re-centers the human and moves towards a digital justice approach (Gieseking, 2020; Cowan & Rault, 2020) for engaging with historical youth data.
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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 | 0.153 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.013 | 0.041 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".