Collective value and expectancy: the influence of group-based perception of personal goals on behaviour and affect
Bibliographic record
Abstract
In many contexts, belonging to a group impacts the pursuit of one's goals. Research on motivation has shown that both the value that one personally attributes to a goal and the personal expectancy of attainment of that goal predict successful goal-oriented behaviour. Building on expectancy-value theories of motivation, I introduce the notions of collective value and collective expectancy. Two experiments were designed to test: (1) whether collective value and expectancy regarding a goal (in this case, initiating a romantic relationship) could predict goal-oriented behaviour (in this case, leaving contact information to be paired with a partner) and (2) if a collective/personal discrepancy in value or expectancy would lead to poorer affect. Experiment 1 (n = 69) tested the effect of collective and personal values, whereas Experiment 2 (n = 71) tested the effect of collective and personal expectancies. The first hypothesis – which stated that collective value and expectancy would predict goal-oriented behaviour, even when considering personal value and expectancy – was not supported. The second hypothesis, which stated that a collective/personal discrepancy value or expectancy would lead to poorer affect, received support, as participants in discrepant conditions experienced significantly lower positive affect. The contribution of both experiments is discussed, in terms of their implications for research on the academic underperformance of certain minority groups, the social dysfunction that characterizes certain Native communities in Canada and the influence of group-based perceptions on pursuing romantic relationships.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".