Free Synthesis Essay Examples & Tips for Students

Universities adopted generative writing tools faster than they wrote policies for them. Ferrante argues that the effect of the technology on undergraduate learning depends less on the software than on the assignment it is used for, observing that “a tool that drafts a paragraph is a threat to a summary task and an irrelevance to an oral defense” (214). Okonkwo and Vance reach a different conclusion from a survey of writing instructors, reporting that most respondents saw declining engagement with course readings once drafting tools became common (88). Lindqvist treats the tools as neither threat nor aid, but as a reason to reassess what an undergraduate essay is supposed to measure (33). Read together, these accounts suggest that artificial intelligence has not devalued undergraduate writing so much as exposed how much of it was already measuring the wrong thing.

The disagreement between Ferrante and Okonkwo and Vance is narrower than it first appears. Ferrante’s claim is conditional, since the harm he describes depends on assignment design. Okonkwo and Vance survey a population of instructors who had not redesigned their assignments, which is precisely the condition under which Ferrante predicts harm (Ferrante 214; Okonkwo and Vance 88). Their results therefore read less as a refutation of Ferrante than as a demonstration of his conditional.

Lindqvist gives that overlap a purpose. If a course’s essays can be produced without reading the assigned material, then the essay was testing fluency rather than comprehension, and the tool has only made the substitution visible (33). This is a stronger claim than either of the other sources makes, and it is not fully supported: Lindqvist offers no evidence that the affected assignments were poorly designed rather than simply difficult to defend under new conditions.

The three sources still converge on one practical point. None of them recommends prohibition, and all three locate the decision with the instructor rather than the institution (Ferrante 216; Okonkwo and Vance 91; Lindqvist 37). The useful question for undergraduate writing is not whether students will use these tools, but what an assignment should ask for once they do.

Carbon pricing has become the default policy instrument in wealthy democracies, and the research on it is less unified than that popularity suggests. Marchetti and Duval (2020) found that jurisdictions with a price floor above roughly $40 per ton reduced industrial emissions measurably within four years, while jurisdictions below that level showed no detectable change. Ashworth (2022) accepted those results but argued that the political durability of a carbon price matters more than its opening level, since prices that provoke public opposition are usually repealed before they take full effect. Nakamura et al. (2021) approached the question from the other direction, reporting that direct regulation of the electricity sector delivered faster reductions than pricing in every case they examined. Taken together, the three studies support a conclusion none of them states alone: carbon pricing works when it is high enough to matter and stable enough to survive, and those two conditions pull against each other.

Marchetti and Duval (2020) and Nakamura et al. (2021) are often read as opposed, but they measure different things. The first examines industrial emissions across a mixed economy; the second isolates electricity generation, a sector with few producers and heavy existing regulation. Speed of reduction in a concentrated sector is not evidence about the whole economy, and neither team claims that it is.

Ashworth (2022) explains why the higher prices Marchetti and Duval recommend remain rare. A price set high enough to change industrial behavior is also high enough for households to notice, and revenue return schemes have not reliably prevented the political reaction that follows. The argument is persuasive but incomplete, because Ashworth draws on three repeal cases and treats them as representative.

The practical implication is a question of sequence rather than a choice between instruments. Sector-specific regulation of the kind Nakamura et al. (2021) describe can deliver early reductions while a carbon price is raised gradually, which is closer to what the longest-running programs have actually done.

References

Ashworth, T. (2022). Durability and design in carbon pricing. Journal of Environmental Policy, 41(2), 155–178.

Marchetti, L., & Duval, R. (2020). Price floors and industrial emissions: A four-year panel study. Climate Economics Review, 12(3), 201–229.

Nakamura, S., Bell, J., & Oyelaran, A. (2021). Sectoral regulation and the pace of decarbonization. Energy Policy Studies, 33(1), 44–71.

The case against standardized admissions tests has been made so often that its strongest opponents are now the more interesting ones to read. Halloran documents that test scores predict first-year college grades less well than high school GPA does, and that the gap widens for low-income applicants (12). Prieto and Weiss report the opposite pattern at selective institutions, where scores added predictive power precisely for applicants from under-resourced schools whose grades were compressed near the top of the scale (47). Chen, reviewing both bodies of work, concludes that the disagreement is really about what admissions is for (203). The evidence supports a narrower claim than either camp usually makes: standardized tests are a poor general measure of preparation and a useful specific one, and the difference depends on what the test is being compared against.

Halloran’s finding is the stronger of the two on its own terms, resting as it does on a larger and more varied sample (12). A critic might reasonably object that predictive validity for first-year grades is the wrong outcome to measure, and that a test defended as a fairness mechanism should be judged on whether it identifies students who would otherwise be overlooked. The objection has real force. It does not, however, rescue the broad claim that scores measure preparation, because Prieto and Weiss locate the added value only in a narrow band of applicants and only where grade inflation had flattened the alternative signal (47).

Chen’s reframing is what makes the two findings compatible (203). An institution trying to admit the students most likely to succeed has little use for a weak second predictor. An institution trying to find students whose records understate them has a real use for one. Both can read the same evidence and reach different decisions without either of them misreading it.

What the sources do not settle is the cost side. None of the three examines the time and money families spend preparing for these tests, and that objection has driven most recent policy change on its own.

Wait times are the standard argument against publicly funded healthcare and the standard argument for reforming it, which suggests the measure is doing too much work. Vasquez (2019) compared waits for elective procedures across eleven high-income systems and found that single-payer systems averaged longer waits for non-urgent surgery but shorter waits for primary care. Lindberg and Adeyemi (2021) added a distributional layer, showing that in mixed systems the average wait concealed a wide spread, with insured patients waiting far less than uninsured ones. Doran (2023) argued that neither finding settles anything without a stated priority, since a system optimized for average speed and one optimized for equal access will produce different numbers on purpose. Together the three studies support a claim about measurement rather than about policy: comparing healthcare systems by average wait time rewards whichever system serves its best-covered patients fastest, which makes the measure unfit for the argument it is usually asked to win.

The results in Vasquez (2019) are less favorable to critics of public systems than they are usually reported to be. The longer waits appear in one category, elective surgery, while the shorter primary care waits affect a far larger share of patients in any given year.

Lindberg and Adeyemi (2021) explain why the averages mislead. A mean drawn from a population with unequal access describes no actual patient, and the spread they document is wide enough that the mean falls between two distinct experiences rather than summarizing either one.

Doran (2023) is the source that turns these findings into an argument, and the weakest link in that argument is its assumption that priorities are chosen deliberately. Systems accumulate their trade-offs across decades of separate decisions, and a stated priority is often a description written after the fact.

The comparison worth making is therefore narrower and harder: waits for a defined procedure, within a defined population, with the coverage rate reported alongside. Vasquez (2019) comes closest to that standard, which is one reason those results resist the summaries built on them.

Platform companies are asked to be neutral carriers and responsible publishers at the same time, and most of the ethical argument about them lives inside that contradiction. Aldridge argues that recommendation systems make the neutrality claim untenable, since ranking content is an editorial act whether or not a human performs it (61). Novak and Terzi resist that conclusion on grounds of scale, holding that a system processing billions of items cannot exercise editorial judgment in any sense that would carry moral responsibility (118). Baptiste rejects the framing altogether and proposes that responsibility attaches to the design of the ranking objective rather than to individual decisions about content (9). Reading the three together supports Baptiste’s relocation of the question: the ethically significant choice is made once, when a company decides what its system should maximize, and every ranking downstream follows from it.

Aldridge and Novak and Terzi argue past each other because they use the term “editorial judgment” differently (Aldridge 61; Novak and Terzi 118). Aldridge means the act of ordering content by some standard. Novak and Terzi mean deliberation about a particular item. Both descriptions are accurate, and only the first is relevant to whether a company is responsible for what its system promotes.

Baptiste’s proposal survives that clarification because it does not depend on either sense of the term (9). A company that sets engagement as its objective has chosen a standard whose effects it can predict, and predictability is enough to ground responsibility without requiring deliberation over individual posts.

The conclusion holds, but it is narrower than Baptiste sometimes suggests. Locating responsibility in the objective explains what a company owes its users; it does not tell a regulator how to verify which objective a private system is actually optimizing for, and Baptiste offers no method for that (12). The argument establishes where to look rather than what to do.