“Thinking Hard or Hardly Thinking?” Objective vs Subjective Elaboration and Resistance to Persuasion
Bibliographic record
Abstract
Attitudes that are formed through extensive elaboration tend to be more resistant to persuasion compared to attitudes based on less elaboration (Petty & Cacioppo, 1986). However, the measures researchers use to assess elaboration can vary; for example, a given researcher may use objective measures (e.g., those that capture the actual extent of message-relevant thinking, such as thought listings) or subjective measures (e.g., those that capture self-reported perceptions of elaboration, such as rating scales). Although both approaches are used, the distinction between objective and subjective measures of elaboration is not always explicit, which may be an issue given the unique roles these different types of measures may play within a given research context. The current research sought to examine if objective and subjective elaboration represent distinct constructs that independently or jointly contribute to resistance to persuasion. Study 1 manipulated objective and subjective elaboration separately to test the ability of subjective elaboration to promote persuasive resistance in the absence of objective elaboration; the results showed no clear differences in resistance outcomes based on variation in the different types of elaboration. Study 2, which was similar to Study 1, employed a 2x2 factorial design to fully cross the objective and subjective levels of elaboration to comprehensively examine their main and interactive effects on persuasive resistance. Although subjective elaboration showed marginal effects on the attitude and intention outcomes, they were in the opposite of the predicted direction and no other meaningful effects emerged. Together, these studies do not provide clear evidence for the unique roles that objective and subjective elaboration likely play in resistance to persuasion. Several limitations are noted, such as potentially weak manipulations, as well as possible interventions to improve future studies in this line of research.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".