Planet of fixers? Mapping the middle grounds of independent and do‐it‐yourself information and communication technology maintenance and repair
Bibliographic record
Abstract
This paper explores the geographical distribution of independent and do‐it‐yourself information and communication technology maintenance and repair (INDIY ICT M&R) activity around the world. It examines a large set of Google Analytics data pertaining to users of free, open‐source online repair manuals provided by iFixit, a US‐based organisation that develops the free manuals, sells tools and components, and also engages in technical education and policy advocacy. The paper draws on three years of available user data (2016–2018). Over this time period the total user base of iFixit's manuals grew from over 1.3 million users to more than 4.1 million users across the planet. However, counter to what might be expected, the global distribution of iFixit users does not systematically co‐vary with internet access rates or with the population size of locations. The results reported here, while partial, are valuable in that they demonstrate both a globally distributed phenomenon and high‐resolution location patterns of INDIY ICT M&R activity. Mapping the extent and spatial patterning of such activity is a jumping off point for the kinds of qualitative analyses needed to elucidate the how's, the why's, and the meanings of the observed uneven distribution patterns. More broadly, the results suggest fruitful directions for deeper analyses and research into both pragmatic questions about ICT maintenance and repair (such as their social, economic, and environmental significance), as well as more speculative questions about how and why the fates of ICT within and between production, use, and discard stand in for dreams of technological futurity and nightmares of social and environmental breakdown.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".