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Record W4362509075 · doi:10.1353/rvt.2023.0013

Something About Shoes

2023· article· en· W4362509075 on OpenAlexaboutno aff
Diane Gottlieb

Bibliographic record

VenueRiver teeth · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicArchitecture, Design, and Social History
Canadian institutionsnot available
Fundersnot available
KeywordsDuskYardPorchHorseback ridingDirtArchaeologyArtVisual artsGeographyArt historyCartographyEcology

Abstract

fetched live from OpenAlex

Something About Shoes Diane Gottlieb (bio) At the end of summer in 1995, we left the suburbs for Woodstock. The house, on Allen Drive, was built in 1950. Ten years before I was born. Eight before my husband Jay. Acres of woods behind us, and set way back from the road, we couldn’t hear the odd car passing. Only the sounds of rustling leaves in the wind, rain against windows. Twigs snapping underfoot when we collected firewood in the fall. Sometimes coyotes howled in the night. Our dogs had little to bark at, but they loved to chase the deer. We’d see a herd, ten, twelve of them, in the early mornings, at dusk, among tall, thin pines, too many to count, their deep green scent, telling us they’d keep guard. To protect us from black bears, which were numerous our first fall in Woodstock, town workers posted signs warning residents to place trash barrels far from their houses. Measures we took to keep ourselves safe. Trash barrels and bears. Like seatbelts and cars. Jay always took the trash out to the edge of the road. 21 Allen Drive was the biggest house I’d ever lived in. Center-hall colonial. Four nice-sized bedrooms. Brick and wood frame. Our realtor said it had good bones. My Labrador once brought a huge bone into the house. Carried it in her mouth. Maybe a femur. Probably a deer’s. [End Page 45] Had it died of natural causes? Shot dead by a hunter’s bullet? Struck by car before wandering back into the woods? Our master bedroom had a walk-in closet. It could have been a sleep-in closet—it was that big. One side for my clothes. Sweaters, warm sweats, fleece—I run cold. The other side for Jay’s. White, starched shirts, hanging stiff in dry-cleaner-plastic. Several wool suits—navy pinstripe, gray. Shoes. Black loafers. Brown wingtips. Sneakers. Rollerblades. Slides. Several polos and T-shirts, faded pairs of jeans. So much clothing to get rid of after he died. After the car crash that killed him just four months after we moved into the house on Allen Drive, I’d often run my hands over Jay’s shirts, press my face into his suits, the wool still fresh with his scent. I wondered when the right time would come to box the clothes. Give them away. Pass his scent on to someone who thrifted, who bought their clothes from Goodwill. They’d wear him when they put on a jacket, smoothed out a crease. His neck would touch theirs, as they buttoned the top button, as they added a tie. I’d always avoided thrift shops. I never wanted used clothes. I feared absorbing a stranger’s energy. Maybe she was an anxious stranger, or angry, unkind, depressed. Maybe she was dead. I didn’t want to take that chance. Even after I’d laundered the clothing, some part of the past would remain in the weave. Shortly before we moved to Woodstock, I watched a woman as she walked in the aisle of our old synagogue with her two young kids. I’d heard she’d just lost her husband—I don’t know how. I turned to Jay. Told him how incredibly sad I was. Everyone feels some degree of sadness, I imagine, when they face people in mourning. But this sadness made its way into my bones. I couldn’t shake it. Maybe it was a measure I took to prepare myself. Maybe I had some inner knowing we would be next. Another woman from our old neighborhood in the suburbs lost her husband in the ocean two years earlier. She, her husband, and her son were vacationing in Montauk, when he had a heart attack, body surfing a small wave. I called her once to check on her. She told me she still had her husband’s dirty laundry in the hamper. His shoes on a mat by the door. Joan Didion wrote about holding onto her husband’s shoes after he died. There’s something about shoes. [End Page 46] One morning, around six months after Jay died...

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)0.005
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: none
Teacher disagreement score0.087
Threshold uncertainty score0.290

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0050.006
Open science0.0010.003
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0870.041

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 head0.044
GPT teacher head0.243
Teacher spread0.199 · 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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