Bibliographic record
Abstract
I'VE BEEN ALLERGIC my whole life. As a child, I was sensible. I kept my distance. I was terrified of the black Labrador next door, his whipping tail and sharp white teeth. But in my twenties, like an idiot, I fell in love. Dog love, requited but impossible. They love me because they love everybody, and I love them even though their fur makes my hands break out in a fierce rash and their drool turns my cheeks red and swollen like a squirrel's, like I'm hoarding nuts for winter. I let dogs lick me anyway, and hoard their love. My partner is even more allergic than I am. Dogs, cats, dust, pollen, ragweed, lotion, detergent, peanuts, all nuts. His allergies are bad in spring and summer from the plants outside, bad in fall and winter from the recirculating indoor air. We go to friends' houses with pets and his lungs ache for days. We halfheartedly research lizards, birds, fish. I can't muster any interest in anything that wouldn't be interested in me, in repaying my care with companionship. I understand this to be the purpose of a pet, even though, or perhaps because, I've never had one. I shudder at pictures of hairless dogs online. Todd sneezes in the next room. I decide to volunteer. On the shelter application, I am asked to rank my choices: Cattery? Small critters? Website? Clerical? I write: 1.) Dogs 2.) Dogs 3.) Dogs I have to wait several months for an open orientation session, and when the day arrives the crowd squeezes into a large, rubber-matted room where obedience classes are held. It's March in west Michigan, and we stomp so much snow off our boots that the floor mats become saturated, covered in slush and burbling where we step. The staff don't care--the entire building is resolutely utilitarian, cinder block and concrete. There isn't a floor in the shelter, in either the animals' areas or the humans' offices, that couldn't be cleaned by dumping buckets of soapy water straight onto it. In the kennel area, this is exactly what staffers are doing, towing mop buckets and red wagons filled with pails of kibble to refill dog bowls, pails of drying feces scraped out of the kennels. The dog room is cacophonous, several aisles of large wire cages separated by gates to foil escapees. Tinny classical music plays under the insistent barking. Our tour continues to the admitting wing, where vet examinations and euthanizations are performed. We pass the behavioral testing room, the reception area, the cattery. There is no small critter room, so the critters' cages sit on open shelving near the cats, which glare through Plexiglas at the mice and hamsters. Back in the obedience room, the volunteer coordinator explains the rigid hierarchy: all volunteers must start with Reading with Fido, before ascending to Dog Walking, Jogging with Fido, or Puppy Petter. Each weekly shift, I am supposed to place a single blanket and toy on the floor of one of the small adoption-counseling rooms. I select a dog from the kennels, loop a lead around its head, guide it into the counseling room, and ignore it. I don't read to the dog, I am told, just quietly to myself, so the dogs can accustom themselves to being ignored. Since every dog at the shelter has been abandoned or surrendered, Reading with Fido seems to me like rubbing salt in a wound. But the dogs must impress people in these adoption rooms, have blown chances with prospective families by being too exuberant, too unruly. The constant disappointment of Reading with Fido teaches them to make a calm impression. They learn that their people will want to eat dinner and watch TV, not always walk or play. The dogs learn that they are not the center of humans' lives, even though we are inevitably at the center of theirs. For two hours every week, I put these dogs at the center of mine. Ivan, Sakura, Jasper, Ruby, Buster, Bostwick, Christmas, Wolfy, Miller. Fat old Miller, who looks at the sad blanket and single toy like it's Christmas morning, like I'm Santa Claus. …
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.357 | 0.106 |
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".