Notice bibliographique
Résumé
Should unmanned aerial systems (drones) be used for hunting? 1 Edward Hanna "We need to establish rules regarding these fast-changing technologies to make sure that people understand that their use … is not appropriate or ethical.Use of this equipment violates the principle of fair chase because it gives hunters an unfair advantage over wildlife."(The Outdoor Wire 2015) Many readers of this journal will read this quote and think "So what?"If that is your reaction, you are not alone.Few readers of J. Unmanned Veh.Syst.are probably hunters; even so, many (if not most) hunters and non-hunters are likely to agree with this statement (Duda and Criscione 2014).This editorial explores the "so what" of regulating the use of unmanned vehicle systems (UVSs), and in particular, "drones" and other emerging technologies, for hunting.This issue has significant implications for the future development and use of UVSs, regardless of whether you agree or disagree with this quote and whether you hunt or not.This editorial explores the ethics of new technologies, what limitations should be placed on their use and what are legitimate reasons for limiting their use.Our modern society is characterised by an explosion of technological innovation; the likes of which has never before been seen in human history.UVSs are just one small branch in the rapid technological change that is occurring throughout society.An interesting characteristic of this explosion is the complex interactions and feedbacks across diverse fields of technology.In the case of UVSs, these include advances in remote control systems, miniaturisation, camera and other sensing systems, imagery analysis software, low-weight, high-tensile-strength materials, and sustainable solar or other power sources.These interactions are similar to the chemical reactions in an explosion; simultaneous positive feedbacks cause things to progress increasingly rapidly.But this explosion in technological innovation is not unconstrained.Perhaps the greatest constraint is people and their aversion to change.Nowhere is this tendency more evident than when it comes to the use of new technology as part of traditional activities with importance in human history.Hunting is an excellent example of where this resistance to change is strong.Much can be learned from this resistance to new hunting technologies.These lessons have broad application in other fields when it comes to the adoption and regulation of new technologies within society.The opening quotation is from New Hampshire's Fish and Game Law Enforcement Chief and relates to a legislative proposal under consideration (and recently approved) to ban emerging technologies including drones, "smart" rifles, and live-feed cameras for hunting (New Hampshire Fish and Game Department 2015).New Hampshire is not alone; some or all of these technologies have already been banned in a number of other jurisdictions.Indeed, Arkansas (along with other states) has changed their constitution to guarantee the right to hunt, fish, and trap; but only using "traditional methods" (State of Arkansas 2011).What qualifies as a traditional method is open to interpretation, but the intent is clear; namely, to constrain the use of new hunting, fishing, and trapping technologies.But are these just and prudent constraints and prohibitions?The primary rationale for banning the use of drones for hunting in New Hampshire and most other jurisdictions is fair chase.The appropriateness of using the ethics of fair chase to establish
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,004 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,004 |
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 source (Gemma direct ou Codex distillé), 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 ».