CRISP: Crowdsourcing Representation Information to Support Preservation: Paper - iPRES 2012 - Digital Curation Institute, iSchool, Toronto
Bibliographic record
Abstract
In this paper, we describe a new collaborative approach to the collection of representation information to ensure long term access to digital content.Representation information is essential for successful rendering of digital content in the future.Manual collection and maintenance of RI has so far proven to be highly resource intensive and is compounded by the massive scale of the challenge, especially for repositories with no format limitations.This solution combats these challenges by drawing upon the wisdom and knowledge of the crowd to identify online sources of representation information, which are then collected, classified, and managed using existing tools.We suggest that nominations can be harvested and preserved by participating established web archives, which themselves could obviously benefit from such extensive collections.This is a low cost, low resource approach to collecting essential representation information of widespread relevance.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.014 |
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".