Bibliographic record
Abstract
本稿では，筆者が運営しているリユース店3店舗の事例に基づき，需要の多い修理の内容や利用者の実態，またコロナ禍前後の利用者や修理内容の変化について報告する。コロナ前には冠婚葬祭用衣類等，高価な衣類のお直しや紳士革靴，婦人ピンヒール等の修理が多かったが，コロナ後にはこれらが激減した。高齢者のマインド変化やリモートワークの増加等が影響しているのではないかと考えられた。またコロナ禍で激減した需要は少しずつ回復してきているが，高齢者の客足の回復が遅れている。そこで，高齢者施設や個人宅を巡回訪問し，お直しを行うサービスを開始した。今後，他の支援ニーズにも対応する予定で，お直しの需要の掘りおこしのみならず，高齢者の安心・安全の確保にも貢献する事業としていく予定である。そのほか，修理サービスにおいてコミュニケーションが重要であること，次世代への技術の継承や経営改善等が今後の課題であることを述べる。
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".