WEB HISTORIES IN THE MAKING: WEB ARCHIVES & THE LOGICS OF PRACTICE
Bibliographic record
Abstract
Historically-situated accounts of the Web have a long history within the field of internet studies. Drawing on diverse methodologies and forms of data, web histories of platforms, cultures and communities of practice have illuminated the rich, but often transient and shifting nature of life online. Many web histories rely upon researchers capturing, collecting, and generating their own data through time, though some have also engaged with web archives as a means for studying the past online. However, web archive data have never fulfilled the requirements of positivist ideals such as ‘representativeness’ or objectivity, and the methodological consequences of this observation currently do not go far enough. This panel aims to shift and reframe current discussions of the ‘promise’ of web archives for web historiography, towards identifying what underlying logics or ideals drive and motivate various actors engaged in this work. We argue that not only do the logics underpinning the practices of collecting and archiving the Web deserve further attention, but also the practices of internet researchers who aim to use these materials for studying the Web. Each paper contribution in this panel builds on web archive criticism by situating archived web material as fundamentally tied to the logics of practice. These underpinnings affect not only the formation of web archives, but also the methodological approaches researchers take. We therefore suggest new ways for conceptualising the ‘doing’ of web histories, tying them to an assemblage of people, practice and data that shape how we can come to understand the Web.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.009 | 0.031 |
| Scholarly communication | 0.032 | 0.034 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".