Right-to-repair - right or not right?
Bibliographic record
Abstract
Right-to-Repair er et af reparationsbevægelsens krav, og EU anvender det også nogle gange fejlagtigt om sine nye fælles retningslinjer for reparationer. Artiklen giver et overblik over den seneste udvikling inden for Right-to-Repair i EU, Frankrig, Canada og Australien og peger på nogle problemstillinger med betydning for effekten heraf. Mens fokus i EU, Frankrig og Canada om Right-to-Repair hidtil mest har været på forbrugerprodukter, er der i Australien kontroverser om retten for landbrugere til hurtigt og billigt at kunne få repareret landbrugsmaskiner og dermed undgå produktionstab. Analysen viser, at Right-to-Repair - retten til at kunne reparere eller kunne få repareret et produkt, som man ejer - kan ses som et spørgsmål om: - Udbud af reparérbare produkter - Adgang til troværdig information om et produkts reparérbarhed og holdbarhed, så der i en købssituation er mulighed for at vælge det mest holdbare og reparérbare produkt - Adgang til information og værktøj, der muliggør reparation, så ejeren eller en uafhængig reparatør kan reparere ejerens produkt, og monopoldannelse på reparation af produkter kan undgås - Mulighed for reduktion af udgiften til reparation gennem direkte eller indirekte tilskud for at gøre reparation mere økonomisk overkommelig og attraktiv sammenlignet med prisen for et tilsvarende nyt produkt
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.142 | 0.065 |
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".