Bibliographic record
Abstract
To understand how manufacturer-imposed restrictions on repair can affect people’s daily lives, consider a grain farmer, Alex,* with a family farm 150 kilometres north of Brandon, Manitoba. When agricultural equipment needs routine maintenance or repair, Alex faces the difficult choice of whether to do the repairs or call the manufacturer-authorized repairer to fix the equipment, a costly appointment that may require a wait of days or weeks, which is especially problematic during planting or harvest seasons. It’s not a question of repair skills, as Alex is an experienced mechanic who routinely fixed tractors before they became computerized. However, with software increasingly incorporated into tractors, Alex encounters manufacturer-imposed locks protected by copyright law that make it difficult, sometimes impossible, to diagnose or fix the equipment. Fortunately for Alex and other farmers, the Canadian government passed two bills in November 2024 that amend the Copyright Act to make it easier to repair software-enabled products (Bill C-244) and ensure interoperability among products operating via software (C-294). Despite Canada’s recent progress on the right to repair with these laws, this case study argues that the questions of who has the right to repair and under what conditions remain core concerns for anyone who purchases and uses software-enabled devices. *Alex is a fictional character created to illustrate the experience of farmers and other users of IoT products.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.030 | 0.011 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".