For cross-text questions, first summarize each text’s main claim in your own words, especially any bolded or quoted portions. Then decide whether the second author agrees, disagrees, or partially agrees with the first, and on what grounds (e.g., methods, evidence, limits). Finally, scan the choices for the one that accurately captures both the stance (agree/qualify/disagree) and the specific reasoning or conditions mentioned in the second text, rejecting options that exaggerate, oversimplify, or contradict the details given.
Hints
- Hint 1
Locate the key claim in Text 1
Reread the bolded portion of Text 1. What is the author saying must be done with volunteer data when making regulatory decisions?
- Hint 2
Contrast the overall positions of the two texts
Ask yourself: Does the author of Text 2 think volunteer data should be thrown out, or used in some way? Look closely at phrases like “wastes useful signal” and “meaningfully augment official monitoring.”
- Hint 3
Check how strong each answer choice is
Notice which options say volunteer data should be entirely rejected, fully replace official data, or be adjusted and combined with official monitoring. Which one best fits the details and example given in Text 2?
Step-by-step
- Step 1
Pinpoint what Text 1 is asserting in bold
Focus on the bolded sentence in Text 1: it says that “the only defensible path is to discount volunteer-collected datasets when making regulatory decisions.”
That means the author of Text 1 is making a categorical (absolute) claim: because of bias and uneven sampling, volunteer data should not be used for regulation at all; they should be discounted (essentially ignored) when making regulatory decisions.
- Step 2
Summarize Text 2’s overall stance on volunteer data
Now look at the first sentences of Text 2:
- The author agrees that concerns about bias in citizen-science data are “justified”.
- But then says “discarding those data wastes useful signal.”
Then the author explains that:
- Modern models can adjust for uneven effort, detectability, and reporting tendencies.
- Combined with some ground-truthing (independent checking), volunteer observations can “meaningfully augment official monitoring.”
- The river-watch example shows volunteer data helping managers issue earlier advisories after adjusting and validating the reports.
So, Text 2 is not saying “these data are perfect,” but is saying they can be made useful for regulation with proper methods, instead of being thrown away.
- Step 3
Clarify how Text 2 would respond to Text 1’s bolded claim
Compare the two positions directly:
- Text 1: Because of bias, the only defensible path is to discount volunteer data in regulation.
- Text 2: Bias concerns are real, but throwing out the data “wastes useful signal”; with statistical adjustments and validation, volunteer data can help regulatory decisions.
So Text 2 would disagree with the idea that the only defensible approach is to discount the data. Instead, Text 2 argues that, in many situations, these data can be used in regulation if handled carefully with appropriate tools.
- Step 4
Match that response to the answer choice
Now pick the option that says Text 2 would reject the absolute “only defensible path is to discount” claim and instead argue that volunteer data, once adjusted and cross-checked, can be reliable enough to inform regulation in many cases.
That matches Choice A) By disputing the categorical claim and contending that statistical adjustments and cross-validation can render volunteer data reliable enough to inform regulation in many cases.