Thinking through death and employment: The automatic yet temporary use of schemata in everyday reasoning
Bibliographic record
Abstract
Over the past two decades, the word schema has become increasingly used by scholars studying culture. Viewed largely as a kind of mental shortcut that individuals internalize by means of their various experiences, the concept enables researchers to study how societal-level factors such as norms and values impact individual action by way of shaping individuals’ cognitive structures. However, little attention is given to how and why particular schemata are used in particular situations. Through comparative analysis of two sets of in-depth interviews on the topics of dying and careers, I find that individuals alternate through various schemata as they attempt to answer questions posed to them. I argue that the presence of this alternation weakens assumptions regarding the automaticity of the deployment of schemata in the decision-making process by signaling that schemata may be triggered automatically but used temporarily. In extension, this argument supports the work of cultural scholars and discursive psychologists who both implicitly and explicitly see schemata as flexible, personalized heuristics rather than impersonal, statically shared determinants of thought and action.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.026 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".