영어 모의고사 (자동 편성)

Social media platforms rarely show users a neutral stream of information. Instead, recommendation algorithms ①are optimized to maximize engagement, and content that provokes strong emotion, particularly outrage, tends to hold attention longer than balanced reporting ever could. Over time, users ②exposed to a steady diet of emotionally charged content may come to believe that extreme opinions are far more common than they actually are, simply because those opinions ③dominate their feed. Researchers describe this as a distorted mirror: the platform reflects not society as it truly is, but a version ④shaping by whatever keeps people scrolling. Some platforms have experimented with reducing the visibility of outrage-driven content, only ⑤to see user engagement drop.

Among the many strategies proposed to remove carbon dioxide from the atmosphere, one of the oldest ①draw on an agricultural technique predating modern industry, converting organic material into a stable form of charcoal known as biochar. Biochar is ②produced through pyrolysis, a process in which crop residues or wood chips are heated in an environment with little or no oxygen, ③preventing the material from burning away as in an open fire. This process leaves behind a carbon-rich solid that resists decomposition far longer than raw organic matter, locking carbon in a form remaining stable in soil for centuries. When mixed into farmland, biochar also improves soil structure, ④retaining moisture and nutrients otherwise lost, which can boost crop yields in depleted soils. For example, farmers ⑤cultivating nutrient-poor tropical soils have reported measurable increases in productivity after adding biochar.

Prospect theory proposes that the subjective value of money is not a straight line but a curved function, ①differing sharply from the assumptions of classical economic models. In the domain of gains, the function bends downward as amounts ②increase, so the psychological difference between earning zero and one hundred dollars feels far greater than between earning one thousand and eleven hundred. A parallel pattern appears among losses, where the curve ③bend in the opposite direction, yet sensitivity still fades as the amount grows larger. This tendency, ④known as diminishing sensitivity, explains why a shopper barely notices a ten dollar increase on a two thousand dollar purchase. Researchers illustrate this curvature with graphs ⑤shaped like an S turned sideways, sloping gently near the center and flattening toward both extremes.

Generative AI systems learn to write, draw, and compose by studying ①enormous collections of existing human work, much of which was created without the artists' knowledge or consent. Supporters of the technology argue that this process resembles how a human student learns by studying the masters, ②absorbing style and technique rather than copying specific pieces. Critics respond that the comparison ③holds up perfectly at scale, since a single AI model can absorb millions of works in days. Courts around the world are now being asked to decide whether training an algorithm on copyrighted material counts as fair use or as ④infringement. Until clearer rules emerge, artists, developers, and publishers remain locked in a legal ⑤gray zone that satisfies almost no one.

Facial recognition software has moved rapidly from airport security lines into shopping malls, schools, and even private apartment buildings, promising ①convenience in exchange for a quick scan of one's face. Proponents note that the technology can unlock doors, catch shoplifters, and speed up identity checks far faster than any human guard could manage. Independent studies, however, have repeatedly found that many commercial systems perform ②less accurately on women and people with darker skin tones, a gap that can lead to wrongful identification with serious real-world consequences. Several cities have already reported cases in which an innocent person was ③detained based on a flawed match. Until these systems can prove equally reliable for everyone, critics argue, deploying them at all represents a form of quiet ④discrimination dressed up as ⑤inefficiency.

When engineers design the software that controls a self-driving car, they face a question philosophers have debated for decades: should a crashing vehicle protect its own passenger, or minimize total harm even at the passenger's expense? Surveys reveal an uncomfortable contradiction: most people say a car should be programmed to save the greater number of lives, yet the same people admit they would never buy a vehicle designed to sacrifice its own owner. This suggests that _______________. Manufacturers, regulators, and philosophers must instead agree on transparent rules before autonomous vehicles are allowed to make such decisions.

Imagine two travelers preparing for the same trip: one refuses to fly after watching news coverage of a recent plane crash, while the other books a flight without hesitation. Statistically, air travel remains far safer than driving the same distance by car, yet vivid, emotionally charged images tend to dominate human judgment more powerfully than dry statistics ever could. This tendency, in which people estimate how likely an event is based on _______________, quietly shapes countless everyday decisions. News broadcasts that repeatedly feature rare disasters can leave viewers convinced that such events are common, even though official records clearly show the opposite. Advertisers and politicians alike exploit this pattern, knowing that a single striking anecdote often persuades audiences more effectively than pages of careful statistics ever manage to.

Factories that once employed hundreds of workers on an assembly line now run with a fraction of that staff, as robotic arms handle tasks that used to require human hands. (A) However, critics point out that earlier transitions unfolded over generations, giving workers time to retrain, whereas today's robots are being deployed much faster. (B) Historical evidence offers some comfort: previous waves of automation eventually created new categories of work that no one had predicted. (C) A factory worker displaced this year, therefore, cannot simply wait for a new industry to emerge decades from now.

Consider a large heap containing ten thousand grains of sand. (A) Something has clearly gone wrong along the way, since no reasonable person would call one grain of sand a heap, but pinpointing exactly which single removal transformed a heap into a non-heap seems impossible. (B) Nearly everyone agrees that removing a single grain from such a heap leaves another heap behind, since surely one grain could never make the difference between a heap and a non-heap. (C) Yet if this principle is applied repeatedly, grain by grain, the heap eventually shrinks to a single lonely grain, or even to no grains at all, and logic alone seems to force us to accept that this final result still counts as a heap.

This evidentiary standard, ironically, has made behavioral interventions more rigorously tested than many of the traditional programs they were designed to complement. Government adoption of behavioral insights has generally been governed by a strict evidentiary standard: officials demand proof that a proposed intervention works before authorizing its use. ( ① ) This demand led to the routine use of randomized controlled trials, in which a proposed change is tested on a small population before being rolled out more broadly. ( ② ) Such trials allow policymakers to determine with reasonable confidence whether an intervention produces the intended effect before committing significant public resources to it. ( ③ ) Many long-standing government programs, by contrast, were implemented decades ago without ever being subjected to comparably rigorous evaluation. ( ④ ) Behavioral units within government have, as a result, gradually earned a reputation for evidence-based rigor that extends well beyond their original, narrow mandate. ( ⑤ ) That reputation has, in turn, given behavioral approaches an outsized voice in policy discussions relative to their still-modest budgets.