MétaCan
Menu
Retour à la cohorte
Enregistrement W4395040332 · doi:10.1353/vcr.2023.a925211

Thomas Hughes’s Tom Brown’s Schooldays , ChatGPT, and Academic Integrity

2023· article· en· W4395040332 sur OpenAlexvenueno aff
Tom Ue

Notice bibliographique

RevueVictorian review · 2023
Typearticle
Langueen
DomainePsychology
ThématiqueOptimism, Hope, and Well-being
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésArtAcademic integrityArt historyPsychologySocial psychology

Résumé

récupéré en direct d'OpenAlex

Thomas Hughes's Tom Brown's Schooldays, ChatGPT, and Academic Integrity Tom Ue (bio) Chatgpt is all the rage. In the Chronicle of Higher Education (12 May 2023), Owen Kichizo Terry, an undergraduate student at Columbia University, observes how easy it is "to use AI to do the lion's share of the thinking while still submitting work that looks like your own." Terry advocates for a "massive structural change" in colleges for them "to keep training students to think critically." By this, Terry refers to actively embracing AI's role in the writing process or creating "a split between assignments on which using AI is encouraged and assignments on which using AI can't possibly help." Many a humanist and scientist share Terry's conviction that AI is here to stay, and they have ruminated thoughtfully on its affordances and limitations. In an editorial for our discipline's pre-eminent journal, PMLA, for example, Wai Chee Dimock imagines our co-existence with AI. "Literature from Gilgamesh on," she says, "has taught us about the human assault on the nonhuman world. It has also taught us the art of assisted survival by making kin with nonhuman beings. The emergence of AI at this moment of crisis makes that art all the more urgent" (453). Lauren M.E. Goodlad responds to Dimock, also in PMLA, by describing AI's major drawback, "impressive strides" notwithstanding: "Lacking sentience, emotion, common sense, imagination, and a model of the world, these powerful pattern-finders cannot cognize the data points they extrapolate" (317).1 Meanwhile, in the top science journal, Nature, Eva A.M. Van Dis et al. share Goodlad's reservations, and they similarly suggest the need to put AI in its place: "The focus should be on embracing the opportunity and managing the risks. We are confident that science will find a way to benefit from conversational AI without losing the many important aspects that render scientific work one of the most profound and gratifying enterprises: curiosity, imagination and discovery" (226). [End Page 21] Conversations about academic integrity are not new. Almost two centuries before ChatGPT, the eponymous character of Thomas Hughes's Tom Brown's Schooldays (1857) had vulgus-books. One evening, Tom and his schoolmates Arthur and Martin are beavering away at their vulgus task, "a short exercise, in Greek or Latin verse, on a given subject, the minimum number of lines being fixed for each form" (259). Here's how the assignment works: The master of the form gave out at fourth lesson on the previous day the subject for next morning's vulgus, and at first lesson each boy had to bring his vulgus ready to be looked over; and with the vulgus, a certain number of lines from one of the Latin or Greek poets then being construed in the form had to be got by heart. The master at first lesson called up each boy in the form in order, and put him on in the lines. If he couldn't say them, or seem to say them, by reading them off the master's or some other boy's book who stood near, he was sent back, and went below all the boys who did so say or seem to say them; but in either case his vulgus was looked over by the master, who gave and entered in his book, to the credit or discredit of the boy, so many marks as the composition merited. (259–60; my emphases) Hughes's narrator gestures, through the incantatory use of the expression "seem to say them," at the pupils' inaudible murmurings, and, through their "reading them off the master's or some other boy's book," at their wandering eyes. Cheating, Hughes goes on to suggest, is by no means confined to oral assessments, and he describes four methods by which students complete their tasks. That a master is tasked to come up with 114 subjects a year (i.e., three a week for each of the school year's thirty-eight weeks) ensures that some of them would be repeated. Pupils "meet and rebuke this bad habit" by handing down their exercises so that "the popular...

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,027
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,070
Score d'incertitude au seuil0,235

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,027
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0160,008
Communication savante0,0120,007
Science ouverte0,0020,008
Intégrité de la recherche0,0060,015
Charge utile insuffisante (le modèle a refusé de juger)0,0700,025

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.

Tête enseignante Opus0,051
Tête enseignante GPT0,366
Écart entre enseignants0,316 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueVictorian reviewMême sujetOptimism, Hope, and Well-beingTravaux en français237 207