Notice bibliographique
Résumé
Horse-Racing Fraud in Victorian Fiction Nancy Henry (bio) In his account of English horse racing, A Mirror of the Turf (1892), Louis Henry Curzon writes: There is no other business, perhaps, which offers so many opportunities for successful fraud as horse-racing, and that for the best of reasons: the chicanery that is prevalent does not render those who practice it amendable to the criminal law, turf crimes being without the pale of legal action. (271)1 Curzon reflects that frauds in the late nineteenth century are more subtle and less violent than in the past, when a horse might be “ ‘nobbled’ or ‘got at’ by some person hired for the purpose, or it might be lamed by the farrier, or perhaps poisoned by a stable attendant” (272). In contrast, the “coups” of his own time are planned over months or years, as when bookmakers purchase horses in order to manipulate their odds across multiple races. Such scams may be impossible to detect, much less punish. He concludes that “turf frauds of many kinds, but especially those kinds which entail no penal consequences, are plentiful enough even at the present time” (300).2 Victorian and contemporary historians have established the changing nature of racing fraud, which became more businesslike as the sport became more commercialized.3 Victorian horse-racing fiction represents a distinctive subculture at the intersection of athletic competition and local and global economies of betting. J. Jeffrey Franklin analyzes gambling as “part of the Victorian discourse of money, [treated] as one among the other channels through which capital was circulated in nineteenth-century Britain” (34–35). I argue further that a focus on horse racing, and on fraud in particular, in Victorian fiction contributes not only to the histories of racing and gambling but also to the history of finance and financial fiction. Writers of fiction were particularly drawn to those earlier forms of fraud mentioned by Curzon, such as poisoning and laming, which could only be carried out by someone intimate enough with the horse to get close and do harm. Such acts of sabotage were motivated by vengeance—usually against the owners of the horse. The elements of class conflict and personal vendetta played into the overall themes of Victorian fiction. The problem of how to identify the perpetrator was consistent with that of other financially themed novels. Swindlers such as Charles Dickens’s Merdle in Little Dorrit (1855–57) [End Page 235] or Anthony Trollope’s Dobbs Broughton (also a horse player) in The Last Chronicle of Barset (1866–67) or Augustus Melmotte in The Way We Live Now (1875) disguise their scams through false identities and elaborate schemes. In contrast, tampering with a horse to sabotage a race, on which great sums of money might be wagered, depends on the simple fact that horses cannot testify. Fiction has the capacity to examine the psychology of the saboteur as well as the experience of the horse. In this respect, examining frauds that involve tampering with the equine athlete opens up the study of financial fiction to the realm of animal studies.4 That Curzon sought a variety of terms—fraud, chicanery, and crime—to describe cheating in racing suggests the difficulty of definitions. The term fraud is expansive, encompassing many possible scenarios of dishonest behaviour. Chicanery seems quaint, and crime is inaccurate where the law is not involved. Within sports generally, Graham Brooks, Azeem Aleem, and Mark Button see fraud as a subset of corruption and offer a useful definition: fraud “is based on deception with the intention of securing some advantage—immediate or in the future—and depriving a third party such as individual(s), a small group of people or organisation of honest services or benefits at the expense of other individuals and organisations” (18). I keep this definition in mind while employing the term fraud in cases where a saboteur intends to deprive an owner of a sound horse, and, consequently, to defraud of their money the people who bet on that horse. Furthermore, such fraud may symbolically target an entire class of wealthy horse owners; crucially, it also physically harms the animal, which may or may not recover. With...
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 | 0,000 | 0,000 |
| 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) | 0,031 | 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 ».