Validating ‘belief in occurrence’ for future autobiographical events.
Bibliographic record
Abstract
Belief in occurrence is theorized to play a central role in remembering autobiographical events.Ernst and D' Argembeau (2017) proposed that belief in occurrence also plays a key role in determining the "realness" of future events, or the subjective sense that imagined future events will genuinely occur.They reported data indicating that belief in occurrence for future events arises from the contextualization of imagined future events within autobiographical knowledge, and that such belief signals when simulated events are consistent with expectations and goals.The validity of their findings can be questioned due to the use of a single item to measure belief in occurrence.To further validate these propositions, we expanded an existing belief in occurrence scale to create parallel forms for measuring belief in occurrence for past and future events.In Study 1 (N = 470) participants rated three past or three future events (recent, distant, uncertain).Study 2 (N = 251) replicated Study 1 using French translations of the measures and a within-subjects design in which participants rated a past and a future event.Confirmatory structural modelling indicated that an eight-item belief in occurrence scale was a strong fit to the data, and that item loadings were invariant across event cues and temporal direction for belief in occurrence, spatial characteristic, and autonoetic awareness latent variables.These studies support the view that belief in occurrence appraisals for past and future events draw on overlapping processes.Relationships between the latent variables and other predictors of remembering / forecasting events are discussed.
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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 | 0.018 | 0.311 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".