CRITICAL CARE & THE EARLY WEB: ETHICAL DIGITAL METHODS FOR ARCHIVED YOUTH DATA
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
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.
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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it