Retrocomputing as Preservation: Poster - iPRES 2012 - Digital Curation Institute, iSchool, Toronto
Bibliographic record
Abstract
This project explores the world of retrocomputing, a constellation of largely-though not exclusively-non-professional practices involving old computing technology.Retrocomputing includes many activities that can be seen as constituting "preservation," and in particular digital preservation.At the same time, however, it is often transformative, producing assemblages that "remix" fragments from the past with newer elements or joining historic components that were never previously combined.While such "remix" may seem to undermine preservation, it allows for fragments of computing history to be reintegrated into a living, ongoing practice, contributing to preservation in a broader sense.The seemingly unorganized nature of retrocomputing assemblages also provides space for alternative "situated knowledges" and histories of computing, which can be quite sophisticated.Recognizing such alternative epistemologies in turn paves the way for alternative approaches to preservation.The institutional digital preservation community may have a lot to gain from paying closer attention to retrocomputing.This gain, however, should not just involve looking for ways to make use of the knowledge and labor of retrocomputing enthusiasts.Rather, it is important to recognize the value of their projects on their own terms and ask in what ways institutional efforts can support such projects.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.150 | 0.019 |
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".