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Record W4392459410 · doi:10.1353/psg.2023.a920299

Furniture

2023· article· en· W4392459410 on OpenAlexaboutno aff
Marilyn Abildskov

Bibliographic record

VenuePrairie schooner · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBusiness

Abstract

fetched live from OpenAlex

Furniture Marilyn Abildskov (bio) chairs The Japanese furniture maker specialized in chairs—light and airy chairs that still looked sturdy. He took his time, carving each chair from hinoki cypress. I was told the chairs sold for a lot of money. He’d been married to a Danish woman. According to the story I’d heard, the Danish woman had gone to Tokyo on a whim and then decided to stay. So the Japanese furniture maker married her to assure her status in Japan. By the time I met them, they had relocated to the countryside and been married for ten years. He wore his hair long, pulled back in a ponytail. She, too, had long black hair, which she wore loose and curly. They made an attractive couple, both dark and thin. She taught English. Listening to her speak, you could not detect a Danish accent. When I saw them it was usually at a vegetarian restaurant that foreigners frequented. They rarely sat together. She wore brightly colored shawls over thin shoulders. He smoked. Eventually she returned to Europe. Rumor had it she didn’t know if or when she’d return to Japan. The question of his status, then, remained. Was he married? Soon to divorce? After a few months he began seeing a Canadian woman who taught English at an elite high school in Matsumoto. One night, she told some of us the following story. The furniture maker frequently called her to ask how to spell a certain word. His English was OK, not great. But he needed to communicate in writing with clients who bought his chairs. [End Page 54] She spelled each word he asked for, including “possible.” I could hear her spelling it out in her posh accent: p-o-s-s-i-b-l-e. He thanked her and she thought that was that. There had been nothing unusual in his request. In his business, he corresponded with people from all over the world, using English, the language of international requests. A few days later, she went to his house, as she often did, and saw on the table a letter he had written to the Danish woman, his wife. The handwriting, I imagine, was neat, the spelling perfect. I want to see you as soon as possible. doors Once, on a train ride to the countryside to visit a mutual friend, Ben quizzed me on how long I’d lived in Japan. “Two years and counting,” I said. When would I leave? he asked. I said I wasn’t sure. “How is that possible?” he continued. He knew my Japanese didn’t amount to much. What was I doing here? He was finishing up a year of teaching in Japan and then it was back to his home country, Australia. For life. For good. He couldn’t imagine living in a country where he couldn’t vote, he said. I thought of what I missed—peanut butter; people laughing loudly on public transportation; my little black couch, which I’d stored in my parents’ basement along with the rest of my belongings. But I said nothing. The next weekend, I went to the restaurant whose name I could never remember. The Vegetarian Place, we called it in Matsumoto, where a lot of foreigners hung out. Mid-evening, I slipped away from friends and natto and edemame to use the restroom. As my hand slipped into the moon-shaped handle on the sliding door, it struck me—something so beautiful, I could not bear to leave. Does anyone stay in a country for a certain door handle? tables At a small kitchen table, Chieko and I talked about Snow Country. In high school, she said, she’d hated the man in the novel for having an affair when he had a wife and child back in Tokyo. But this time, reading Yasunari Kawabata’s novel in English, she was struck by how beautifully [End Page 55] written the novel is—how, from the first line, there is a boundary between imaginary and real worlds. “Have I gotten more realistic?” she asked. We talked about infidelity. Interpretation. Silence. A toleration for silence. She said...

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.731
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.000
Scholarly communication0.0050.003
Open science0.0020.004
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.7310.543

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.

Opus teacher head0.061
GPT teacher head0.310
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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