Bibliographic record
Abstract
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
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".