Gone, Gone, but Not Really, and Gone, But Not forgotten: A Typology of Website Recoverability
Bibliographic record
Abstract
This paper presents a qualitative analysis of the recoverability of various webpages on the live web, using their archived counterparts as a baseline. We used a heterogeneous dataset consisting of four web archive collections, each with varying degrees of content drift. We were able to recover a small number of webpages previously thought to have been lost and analyzed their content and evolution. Our analysis yielded three types of lost webpages: 1) those that are not recoverable (with three subtypes), 2) those that are fully recoverable, and 3) those that are partially recoverable. The analysis presented here attempts to establish clear definitions and boundaries between the different degrees of webpage recoverabilty. By using a few simple methods, web archivists could discover the new locations of web content that was previously deemed lost, and include them in future crawling efforts, and lead to more complete web archives with less content drift.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".