Bibliographic record
Abstract
Purpose The paper's aim is to describe the world of retrocomputing, a constellation of largely non‐professional practices involving old computing technology. It seeks to show how retrocomputing serves the goals of collection and preservation, particularly in regards to historic software, and how retrocomputing practices challenge traditional notions of authenticity. It then seeks to propose an alternative conceptualization and suggest new avenues for collaboration between retrocomputing practitioners and memory institutions. Design/methodology/approach The paper is based on extensive observation of retrocomputing projects, conducted primarily online. Findings Retrocomputing includes many activities that can be seen as constituting collection and preservation. At the same time, it is often transformative, producing assemblages that “remix” fragments from the past with newer elements or joining together historic components that were never combined before. While such “remix” may seem to undermine preservation, it also 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 sometimes be quite sophisticated. Research limitations/implications Retrocomputing challenges established notions of collection and preservation. A “situated knowledges” perspective provides a possible resolution. Practical implications Retrocomputing presents memory institutions (and libraries in particular) with an opportunity for new forms of collaboration in collection and preservation of software applications. Originality/value The paper puts at the center the ways in which retrocomputing challenges the established notions of collection and preservation. It offers alternative conceptualizations that suggest new forms of collaboration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.012 | 0.060 |
| Scholarly communication | 0.024 | 0.026 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".