Thinking fast and slow about willingness to communicate: A two-systems view
Bibliographic record
Abstract
How does a person decide whether she or he is willing to communicate?Dual-process theories have been influential in the literature on the psychology of making judgments and decisions.Dual-process theories make a distinction between cognitive processes that are fast, automatic, and unconscious (also called 'experiential' thinking) and those that are slow, deliberative, and conscious (also called 'rational' thinking).The study assesses differences in willingness to communicate (WTC) ratings made based on rational and experiential processes, and differences between native to second language WTC.Data were collected from a sample of 84 students in Iran and 82 students in Canada.Both groups assessed their WTC using English as a second language in Iran and as a native language in the Canadian sample.Data analysis showed that a preference for using rational thinking, as measured by the Rational-Experiential Inventory (Pacini & Epstein, 1999), was correlated with WTC ratings made fast and slow, but only in the second language.We also found WTC ratings were significantly higher when made fast compared to slow, regardless of language group.Pedagogical implications are discussed with advice to teachers how to capitalize on rational thinking and to avoid hesitation in communication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".