Blogs as Objects of Preservation: Advancing the Discussion on Significant Properties
Bibliographic record
Abstract
The quest for identifying ‘significant properties’ is a common \nchallenge for the digital preservation community. While the \nmethodological frameworks for selecting these properties provide \na good foundation, a continued discussion is necessary for further \nclarifying and improving the available methods. This paper \nadvances earlier work by building on the existing InSPECT \nframework and improving its capabilities of working with \ncomplex/compound objects like blogs. The modifications enable a \nmore thorough analysis of object structures, accentuate the \ndifferences and similarities between the framework’s two streams \nof analysis (i.e. Object and Stakeholder analysis) and, \nsubsequently, improve the final reformulation of the properties. \nTo demonstrate the applicability of the modified framework, the \npaper presents a use case of a blog preservation initiative that is \ninformed by stakeholder interviews and evaluation of structural \nand technological foundations of blogs. It concludes by discussing \nthe limitations of the approach and suggesting directions for \nfuture research
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".