Notice bibliographique
Résumé
The Texas Motel Jill Talbot (bio) I don’t think I knew I was going until I pulled onto the highway. That was five miles back. It’s Saturday, mid-morning, mid-summer. I’m forty miles from Dallas on I-35 North. Up ahead, dark clouds drape the sky like a stage curtain. I wonder if I should turn back. When I hear the high guitar notes of Bob Welch’s Sentimental Lady, I turn up the volume. Sentimental gentle wind blowing through my life again. Ranches, acres of clear land, and solar energy fields line both sides of the highway, and traffic thins out as the lanes narrow from four to two. For years I’ve been wondering about the difference between memory and imagination. If there is a difference. I-35 begins in Laredo, Texas, on the Mexican border, and winds vertically through the middle of the country all the way to Duluth, Minnesota. In Texas, the highway cuts through San Antonio, Austin, and Dallas before it runs through the last town, Gainesville, population 16,441. 503.9 miles. The last Texas exit, 504, appears just before the bridge over the Red River, [End Page 51] the Texas/Oklahoma border. I live in Denton off Exit 465A, where the sign of a 7-Eleven rises above the overpass. I read somewhere that Bob Welch wrote Sentimental Lady in the Gorham Hotel in New York City in 1955. That hotel no longer exists. What happens to our stories when the rooms that once held them disappear? Traffic slows, merging to one lane. Ahead, a stout construction worker stands in the middle of the lane with a STOP sign while southbound traffic slowly passes. After a while, he turns his sign to SLOW. I wave as I pass him, thinking about a story I once heard when I lived in Utah for a few years about fifteen years ago. I don’t remember who told me. Maybe a colleague? Maybe a man sitting next to me at a bar? But here’s the story: A construction worker on I-15 outside of Park City stopped traffic. He stood in the middle of the highway, next to the first car waiting in the line. The driver rolled down his window and asked the worker how he was doing. The driver was Robert Redford. I also lived in northern New York for a while, in my late thirties, not far from the Canadian border. The landscape open, hushed. I’d go on drives along the quiet routes in search of abandoned buildings. There are plenty. There’s even a website: “Old Abandoned Buildings of Northern New York.” Collapsed barns, lonely silos, fences struggling to stand. Motor lodges strangled by brush, stone schoolhouses, stores with signs still intact. Pete’s Place. But it was the abandoned tavern on County Road 24 that fascinated me the most. Its faded sign, Turnpike on the stucco wall and above the entrance, wood cutouts of a mug, a martini glass, and a wine glass. A Harley Davidson sticker on the glass door. The place reminded me of a bar in Utah, one with a concrete floor and a sign behind the bar, Memberships $12 a year. A Utah law back then. I’d go afternoons after teaching class, arrive about the same time the salt-and-pepper-haired man did, the two of us setting down the day with the first pint. I don’t remember his name, but I remember his plaid shirts and weariness, his starched Wranglers and [End Page 52] stories, the ones about digging graves, waiting for the last mourner to leave before filling it. The toughest, he once told me, were the ones who stayed until he had stamped the raised earth with a shovel before heading to the excavator parked several feet away. He always felt bad leaving the mourner at the mound. What stories were told from the barstools inside the Turnpike? And how long ago did last call end with raised glasses of farewell before shadows spilled from the door and onto the gravel of the parking lot for the last time? The parking lot where I stood...
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».